Up Down Stairs

## Overview

Up Down Stairs is a dataset for object detection tasks - it contains Upstairs Downstairs annotations for 1,289 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fall Down Det

## Overview

Fall Down Det is a dataset for object detection tasks - it contains Bending Down Up annotations for 3,997 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Down And Feater Detection

## Overview

Down And Feater Detection is a dataset for object detection tasks - it contains Down Feather annotations for 406 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Sitting And Standing

## Overview

Sitting And Standing is a dataset for object detection tasks - it contains Sitting Down And Standing People annotations for 437 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Stair

## Overview

Stair is a dataset for object detection tasks - it contains S annotations for 496 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

• Introduction
• Object Classes
  • Plane
  • Ship

Introduction

This dataset is for object detection, focusing on identifying and annotating planes and ships. The primary goal is to detect these objects in various scenes to assist in automated systems for recognition and tracking.

• Plane: Typically identified by a distinct wing structure and elongated body.
• Ship: Recognizable by its elongated hull and the inclusion of structural elements like decks or containers.

Object Classes

Plane

Description

Planes are generally characterized by their distinct wing structures, elongated fuselage, and sometimes visible tails. They often have a central body from which wings extend horizontally, forming a cross-like appearance from a top view.

Instructions

• Draw bounding boxes around the entire plane, ensuring all visible parts including the wings and tail are included.
• Do not include any reflections or shadows in the bounding box.
• If parts of the plane are occluded, estimate the bounding box to cover the full extent of the plane to the best of your ability.
• Do not label objects if they are too blurry to confirm as a plane or if they fall below the resolution threshold where distinctive features cannot be discerned.

Ship

Description

Ships are discernible by their long, narrow hulls often accompanied by various structures on top like decks or containers. They usually appear elongated with linear or curved features along the sides, often giving a top-down view a rectangular shape.

Instructions

• Draw bounding boxes to include the entire visible structure of the ship, covering both the hull and any superstructures.
• Avoid covering reflections or shadows in the bounding box.
• If the ship is partially occluded, include the unseen parts based on the visible structure.
• Do not label any indistinct shapes or forms that cannot clearly be identified as a ship or if the resolution is insufficient to identify key features.

聲明: 這份研究僅供個人學習使用，若有任何涉及圖片版權的問題，請立即通知我們，我們將迅速下架相關內容。 Statement: This study is for personal learning purposes. If there are any issues regarding image copyrights, please notify us immediately, and we will promptly take down the content.

• Resources: 1. 8891 car: https://www.8891.com.tw/

This dataset is a subset of the whole dataset. The breakdown can be found here: https://bit.ly/3KeLy3o.

I am currently doing my thesis but due to the lack of related dataset available, I decided to find small ones and combine them. If you own any of the small sets and would like to take it down, please send me a messsage in this email: sccraus@up.edu.ph. Rest assured I will only use your dataset for academic purposes only. Thank you.

Overview

• Introduction
• Object Classes
  • Number
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9

Introduction

This dataset is designed to facilitate the task of detecting and recognizing characters on a water meter. It includes annotations for individual digits (0-9) as well as any other textual or numeric information present. The goal is to provide comprehensive labels for training object detection models to accurately identify and interpret the numeric readings.

• Number: Refers to any sequence or individual numeric or alphabetic characters that are not single digits.
• 0-9: Individual digits from 0 to 9 that appear as standalone characters on the water meter display.

Object Classes

Number

Description

The "Number" class includes any sequences or isolated numeric or alphabetic characters that are not part of the specific digit classes (0-9). These can be alphanumeric codes, labels, or any other text visible on the water meter that is not a single digit.

Instructions

• Annotate any sequence of numbers or text that is not a single digit.
• Include all visible parts of the alphanumeric sequence.
• Do not annotate individual digits that are clearly separate and belong to classes 0-9.

0

Description

The digit "0" is represented as a single closed loop character, often circular or oval in shape, found on the water meter display.

Instructions

• Annotate the complete visible form of the digit "0".
• Ensure the bounding box closely encloses the outer edges of the character.
• Ignore partial digits that are not clearly readable as "0".

1

Description

The digit "1" typically appears as a single vertical line, occasionally with a small base or top serif, depending on the font style.

Instructions

• Draw a bounding box around the full visible extent of the digit "1".
• If the digit has serifs, include them within the box.
• Do not annotate shadows or indistinct lines that could be mistaken for "1".

2

Description

The digit "2" usually has a rounded top loop and a descending diagonal stroke ending in a horizontal base.

Instructions

• Capture the entire digit "2", from its rounded top to the bottom base.
• Make sure the bounding box is snug around both the top loop and the bottom line.
• Do not label portions that do not clearly define the character "2".

3

Description

The digit "3" consists of two rounded loops stacked vertically with their centers aligned.

Instructions

• Enclose both rounded loops of the digit "3" within a bounding box.
• Ensure there is minimal space between the edge of the loops and the box.
• Disregard any artifacts that do not contribute to the full shape of "3".

4

Description

The digit "4" appears with a vertical line intersected by a diagonal line forming a triangle and a horizontal base.

Instructions

• Include the vertical, diagonal, and horizontal components within the bounding box.
• Verify the triangle and base are entirely contained.
• Ignore marks that do not complete the "4" shape.

5

Description

The digit "5" features a top horizontal line, a curved back, and a flat base, resembling an incomplete circle with a flat top.

Instructions

• Annotate from the top line through the back curve and base line.
• Align the bounding box tightly around the curved sections.
• Exclude markings that do not complete the recognizably "5" structure.

6

Description

The digit "6" includes a closed loop at the bottom with an open top loop, appearing as a partially twisted circle.

Instructions

• Ensure the bounding box covers both the open top and closed bottom loops.
• The box should encompass the whole digit, avoiding excess space.
• Neglect incomplete loops not forming a full "6".

7

Description

The digit "7" has a flat top line connected to a diagonal descending line, often lacking additional embellishments.

Instructions

• Frame both the horizontal top and the descending line.
• Box should be tight, especially around the junction of the horizontal and diagonal lines.
• Do not include extraneous lines that do not match "7".

8

Description

The digit "8" consists of two equal-sized closed loops stacked vertically.

Instructions

• The bounding box should capture both loops fully, ensuring the character's symmetry is maintained.
• Do not annotate shapes that do not distinctly form an "8".

9

Description

The digit "9" appears as a top loop with a straight or slightly curved descending tail, resembling an upside-down "6".

Instructions

• Annotate both the top round and tail section within the bounding box.
• Ensure the box encapsulates the full shape from top to bottom.
• Exclude lines without a connecting loop or tail completing a "9

Overview

• Introduction
• Object Classes
  • Human
  • Wind Sup Board

Introduction

This dataset focuses on detecting humans and wind sup boards in flooded areas. The images are aerial views, capturing subjects and objects amid water. Two object classes are present:

• Human: Individuals partially or fully immersed in water.
• Wind Sup Board: Paddle boards used on the water surface.

Object Classes

Human

Description

Humans are depicted as seen from above, often showing parts of the body such as the head, arms, or torso. The individuals might be floating or swimming.

Instructions

• Annotate any visible part of the human body observed from a top-down view, focusing on distinctive body parts like the head or limbs.
• Consider whole individuals or only visible sections if the rest is submerged or obstructed.
• Do not annotate any human shadows or reflections.

Wind Sup Board

Description

Wind sup boards are elongated, oval-shaped objects typically seen on the water surface. They may be accompanied by individuals using them.

Instructions

• Draw boundaries around the visible area of the wind sup board, capturing its oval shape from an overhead perspective.
• Differentiate the board from individuals by focusing on its distinct shape, usually elongated and visibly separate from the body's outline.
• Do not include paddles or other equipment not directly attached to the board in the annotation.

Hand Gesture

## Overview

Hand Gesture is a dataset for object detection tasks - it contains Gestures annotations for 1,088 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

• Introduction

• Object Classes

  • 0
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9

• 0: The digit 0

• 1: The digit 1

• 2: The digit 2

• 3: The digit 3

• 4: The digit 4

• 5: The digit 5

• 6: The digit 6

• 7: The digit 7

• 8: The digit 8

• 9: The digit 9

Introduction

This dataset contains images of water meter readings with the purpose of digitizing the numeric values. There are 10 classes representing the digits 0 through 9. Annotators will label the digits as they appear on the meters to facilitate accurate recognition.

Object Classes

0

Description

The digit "0" is characterized by its oval or circular shape, often with a distinctive horizontal thickness.

Instructions

• Annotate the entire visible area of the digit, ensuring the bounding box captures the full curvature without cutting into adjacent digits.
• Do not label if only partially visible or distorted beyond recognition.

1

Description

The digit "1" typically appears as a straight vertical line, sometimes with a short horizontal base.

Instructions

• Draw a bounding box around the full length of the digit, including any visible base. Ensure clear vertical alignment, and avoid labeling if obscured by glare or shadows.

2

Description

The digit "2" has a curved top and straight middle section, finishing with a horizontal or diagonal stroke at the base.

Instructions

• Ensure that the bounding box covers the entire silhouette, from the curved top to the base.
• Avoid labeling if any section is visibly missing or incomplete.

3

Description

The digit "3" is identified by two stacked curved sections without intersecting lines.

Instructions

• Capture both curvatures completely within the bounding box.
• Ensure that no parts are obscured by reflections or meter shadows before labeling.

4

Description

The digit "4" often features intersecting horizontal and vertical lines with a triangle-like top section.

Instructions

• Include the intersecting lines and full top section within the bounding box.
• Avoid labeling if structural lines are affected by wear or visibility issues.

5

Description

The digit "5" combines a prominent upper loop with a lower horizontal stroke and a straight vertical line.

Instructions

• Enclose the loop and strokes fully within the bounding box.
• Ensure separation from neighboring digits, and do not label if substantial parts are unclear or missing.

6

Description

The digit "6" features a closed top loop with an extended lower curve that continues downward.

Instructions

• Draw the bounding box to encompass both the loop and the extended curve, ensuring clarity and complete visibility.
• Avoid labeling partially obstructed digits.

7

Description

The digit "7" is characterized by a horizontal top line connecting to a diagonal downward stroke.

Instructions

• Include the horizontal and diagonal lines entirely in the bounding box.
• Check for clarity and distinction from shadows before labeling.

8

Description

The digit "8" resembles two stacked circles or loops, one above the other.

Instructions

• Ensure the bounding box captures both loops.
• Confirm that the digit is not overlapping with others and is entirely visible before labeling.

9

Description

The digit "9" starts with a circular or elliptical loop at the top, leading into a straight downward stroke.

Instructions

• The bounding box should cover the complete loop and the straight line.
• Do not annotate if any parts are obscured or not clearly discernible.

Here are a few use cases for this project:

1. Vehicle Theft Prevention and Recovery: The Vin detection model can be used by law enforcement agencies to identify and track stolen vehicles by recognizing the VIN numbers in photos or videos taken from cameras, such as traffic or security cameras, when searching for specific missing cars.

2. Vehicle Insurance Claim Processing: Insurance companies can utilize the Vin detection model to automatically verify and validate vehicle ownership during insurance claims by extracting the VIN number from images submitted by customers. This can significantly reduce fraudulent claims and speed up the claim processing.

3. Vehicle Maintenance and Service Tracking: Automotive service centers can integrate the Vin detection model into their customer management systems, enabling them to maintain accurate service records based on the identified VIN numbers. This allows for a streamlined system that ensures each vehicle receives the correct services and parts.

4. Used Car Marketplace: Online platforms for buying and selling used vehicles can use the Vin detection model to identify VIN numbers of listed vehicles, enabling potential buyers to access important information like the vehicle's history report through services like Carfax or AutoCheck. This can help increase buyer confidence and reduce the prevalence of scams in the used car market.

5. Vehicle Registration and Licensing: Government departments responsible for registering and licensing vehicles can automate the process of verifying vehicle identity by using the Vin detection model on photos submitted during registration or renewal. This not only increases efficiency and accuracy but also reduces the risk of human error in copying down the VIN number manually.

Overview

• Introduction
• Object Classes
  • Fasteners
  • Defective Fishplate
  • Fastener
  • Missing Fastener
  • Non Defective Fishplate

Introduction

This dataset is designed to assist in the detection of defects in railway components. It focuses on identifying various conditions of railway fasteners and fishplates. The classes included are:

• Fasteners: Group of multiple fasteners holding down a rail.
• Defective Fishplate: Damaged or improperly aligned fishplates.
• Fastener: A single fastening piece securing the rail.
• Missing Fastener: Locations where fasteners are absent.
• Non Defective Fishplate: Properly aligned and undamaged fishplates.

Object Classes

Fasteners

Description

Fasteners are multiple securing elements visible along the railway track, often appearing in groups.

Instructions

• Annotate the entire group of fasteners, ensuring the bounding area encompasses all visible pieces securing the rail.
• Ensure no single fastener in the group is annotated separately within this class.
• Do not include rail or other components outside the immediate group of fasteners.

Defective Fishplate

Description

Defective fishplates show signs of damage or misalignment directly on the rail joint.

Instructions

• Draw bounding boxes around the entirety of the fishplate area if there is visible damage or misplacement.
• Ensure the bounding box includes visible gaps, cracks, or misaligned joints.
• Exclude surrounding rails or elements that do not show defects.

Fastener

Description

A fastener is an individual securing element attached to the rail, smaller in size compared to the group in "Fasteners."

Instructions

• Annotate each separate fastener visibly securing the rail, ensuring precise bounding around the entire piece.
• Avoid including multiple fasteners in one annotation; each should be individually marked.
• Do not include fasteners that are part of a visibly larger group.

Missing Fastener

Description

Gaps or spaces where fasteners should be present but are absent.

Instructions

• Mark the entire area where a fastener is clearly missing.
• The bounding box should include space gaps or missing elements where installation is evident but absent.
• Do not include areas where fasteners were never intended to be present.

Non Defective Fishplate

Description

Non defective fishplates are properly aligned and undamaged, located at rail joints.

Instructions

• Draw bounding boxes around fishplates showing no visible signs of damage and are correctly aligned.
• Ensure the annotation solely includes the fishplate area without extending to other rail components.
• Avoid marking fishplates with any visible damage or displacement.

Overview

• Introduction
• Object Classes
  • Penguin

Introduction

This dataset is designed to solve penguin detection and segmentation tasks. It includes images of penguins in various environments. The dataset consists of one class:

• Penguin: This class represents penguins, identifiable by their distinctive body shape, beak, and coloration patterns.

Object Classes

Penguin

Description

Penguins are flightless birds with a distinctive waddling walk, known for their tuxedo-like appearance which includes a black back and white belly. They have flippers, a beak, and are usually standing, waddling, or lying on ice or rocky surfaces.

Instructions

• Bounding Boxes: Draw bounding boxes tightly around the penguin, including the entire body, head, and flippers. If the penguin is partially occluded, extend the box to estimate the occluded parts.
• Posture Variations: Penguins may be in different postures such as standing, walking, or lying down. Ensure the annotation covers the full extent of the penguin's silhouette in each posture.
• Do Not Label: Avoid labeling shadows or reflections of the penguins.
• Small Details: Include the beak and flippers within the bounding box. Do not extend the box beyond these extremities.
• Incomplete Visibility: If a penguin is cut off at the edge of the image, the bounding box should end at the image boundary.
• Environment: Penguins may be on snow, ice, or rocky terrain; focus on clearly identifying the penguin itself without marking the environment.

Baby Faces

## Overview

Baby Faces is a dataset for object detection tasks - it contains Babies annotations for 48 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Overview

• Introduction
• Object Classes
  • Pylon

Introduction

This dataset is designed to improve the detection of electric pylons in satellite imagery. The task involves annotating pylons, which are crucial for infrastructure monitoring and maintenance. The dataset contains one class: pylons.

Object Classes

Pylon

Description

A pylon is a tall metallic lattice structure supporting overhead power lines. In satellite images, it appears as a small, distinct grid or network with visible metal patterns. Pylons are typically located in open areas or along direct lines between locations.

Instructions

• Identification: Locate the pylon by identifying a structured, latticed metal pattern. The shape is usually a grid-like structure with a clear top-down view.
• Bounding Box: Encapsulate the entire pylon structure in the bounding box. Ensure that the box captures the full extent of the lattice.
• Visibility: Only annotate pylons that are clearly distinguishable. If the pattern is too small or indistinct, it should not be annotated.
• Partial Visibility: If a pylon is partially occluded by vegetation or other structures, include the entire visible portion unless it’s less than 20% visible.
• Non-Annotated Items: Do not annotate structures that resemble pylons but lack the distinct lattice structure, such as other types of towers or natural formations.
• Clarity: Ensure the pylon is clearly visible and not significantly obscured by shadows or other elements in the imagery.

These instructions should be applied consistently across all images to create accurate and reliable annotations.

Overview

• Introduction
• Object Classes
  • Hex Washer Screw
  • Hexagonal Bolt
  • Philips Screw
  • Pozidriv Screw
  • Torx Screw

Introduction

This dataset is used for object detection of various types of screws and bolts. The goal is to accurately annotate each instance of the following classes: Hex Washer Screw, Hexagonal Bolt, Philips Screw, Pozidriv Screw, and Torx Screw. Each class represents a distinct type of fastener with unique characteristics.

Object Classes

Hex Washer Screw

Description

A Hex Washer Screw is identifiable by its hexagonal head and integrated washer underneath. It is primarily used in exterior applications.

Instructions

• Annotate the entire head of the screw, ensuring the hexagonal shape and washer are included.
• Do not label if only the shaft is visible without the head.

Hexagonal Bolt

Description

The Hexagonal Bolt features a prominent six-sided head. Unlike screws, bolts do not have a washer face.

Instructions

• Annotate the entire head making sure to capture the distinctly thick, flat hexagonal sides.
• Avoid annotation of bolts if they appear worn to the extent of losing their shape.

Philips Screw

Description

A Philips Screw has a cross-shaped recess in the head, allowing for better grip of screwdrivers.

Instructions

• Focus on the recessed cross pattern when annotating.
• Do not annotate if the cross is obscured or not visible.

Pozidriv Screw

Description

The Pozidriv Screw is similar to the Philips Screw but features additional notches that increase driving stability.

Instructions

• Ensure both the cross and the notches are clearly visible and included in the annotation.
• Avoid labeling if the notches cannot be distinguished.

Torx Screw

Description

Torx Screws possess a star-shaped head with six rounded lobes, designed to prevent slippage.

Instructions

• Annotate capturing the star pattern clearly.
• Do not annotate if the lobes are indistinct or worn down beyond recognition.

Overview

• Introduction
• Object Classes
  • Capacitor
  • Capacitor_footprint
  • IC_bottom
  • IC_footprint
  • IC_top
  • LED_bottom
  • LED_footprint
  • LED_top
  • Resistor_bottom
  • Resistor_footprint
  • Resistor_top

Introduction

This dataset focuses on the annotation of electronic components in images, including capacitors, integrated circuits (ICs), LEDs, and resistors. The dataset is used for object detection tasks in surface-mounted device (SMD) scenarios. Each class represents a different component or view of a component, assisting in the training and evaluation of machine learning models.

Object Classes

Capacitor

Description

A small, rectangular component with metallic ends.

Instructions

Annotate the entire body of the capacitor, ensuring the bounding box covers the full rectangular shape including the metallic ends. Do not include any surrounding components or markings.

Capacitor_footprint

Description

Marks on the circuit board indicating where a capacitor should be placed.

Instructions

Draw bounding boxes around the footprint marks, ensuring the box tightly follows the rectangular shape on the board. Do not annotate other component footprints.

IC_bottom

Description

A rectangular component viewed from the side with visible metal pins on one side.

Instructions

Capture the full body and the visible metal pins. The bounding box should encompass the entire length and width of the IC, including pins, without cutting off any parts.

IC_footprint

Description

Circuit board markings outlining where an IC component is to be situated.

Instructions

Annotate the area indicated by the footprint, matching the rectangular shape and aligning with the pin outlines on the board. Exclude non-IC footprints.

IC_top

Description

The top view of a rectangular IC component, often showing brand markings and pin outlines.

Instructions

Draw bounding boxes around the entire visible top surface, including any visible markings and pin outlines. Do not include neighboring components or board markings.

LED_bottom

Description

An LED viewed from the bottom, displaying characteristic flat rectangular shape.

Instructions

The bounding box must cover the entire visible flat side of the LED, including any markers or labels. Avoid including the surrounding board area.

LED_footprint

Description

Footprint marking on the board indicating LED placement.

Instructions

Annotate around the LED footprint, ensuring the full marking is within the box. Make sure no other component footprints are included.

LED_top

Description

A top-down view of an LED showing its small, square shape.

Instructions

Capture the entire top view, ensuring the bounding box fits the square shape tightly. Exclude all other components.

Resistor_bottom

Description

Rectangular surface of the resistor from the bottom with metallic parts visible.

Instructions

Draw bounding boxes to cover the entire rectangular body of the resistor, including metallic contacts. Do not include nearby components.

Resistor_footprint

Description

Footprint on the circuit board indicating where a resistor should be positioned.

Instructions

The annotation should follow the footprint marks, precisely outlining the rectangular shape. Avoid annotating footprints of other components.

Resistor_top

Description

Top view of a resistor, often with a number marking on its surface.

Instructions

Annotate the entire top surface, ensuring to include any visible numbering or markings. Do not include surrounding board space or adjacent parts.

Introduction

The purpose of this dataset is to help localize and classify different defects that are often found on wood planks used in manufacturing, or other wood products. Wood will often have knots, cracks, or holes in it, all of which inform the quality and condition of the wood.

A wood knot comes from the spot a branch once grew out of a tree. The knots can have variation between them, indicating the health of the tree that the plank came from. Specifically, knots can either belong to dead branches or live branches. A knot may also sometimes be cracked.

Object Classes

Crack

Description

This class contains cracks in the wood. It specifically does not contain cracks that occur in knots.

Instructions

Draw a bounding box around the entire crack. If a crack has several clearly distinct sections to it, draw boxes around those individually. Try to make the box as tight around the crack as possible. Cracks will often appear as dark lines in the wood that do not necessarily follow the wood grain. Do not tag cracks that appear in knots, as those belong to the knot with crack category. If a crack extends a long way out of a knot, more than the diameter of the knot, draw a box around the part of the crack not in the knot.

Holes

Description

This class contains holes in wood. The two major kinds of holes are: the hole a termite or nail may leave behind, small and concentrated; or a hole that has the same dark color as a crack but just doesn't go anywhere, having instead a circular shape. These holes can be very small, so look carefully!

Instructions

Draw a tight bounding box around the hole. If there are clusters of holes, mark each individually. Make sure not to tag holes that appear in knots.

Knot with Crack

Description

This is a knot that has a crack in it. It doesn't matter if the knot is dead or alive; if it has a crack in it, it's a knot with crack.

Instructions

Draw a tight bounding box around the knot that has the crack in it. Most of the time, the crack will be contained entirely within the knot. However, if the crack extends outside of the knot, do not enlarge the bounding box to contain the rest of the crack. The important thing is that the box is tight around the knot.

Dead Knot

Description

This knot belonged to a branch that died before the tree was cut down. Dead knots typically have strong discoloration around at least some part of their boundary, indicating that its growth with the tree was poor. Often this discoloration is in the form of a very dark ring around the knot. Other times, there is just a sudden and sharp contrast with the color of the rest of the wood, that isn't necessarily shaped as a ring. The discoloration is sharp and sudden. It can look like the boundary of rot.

Instructions

Draw a tight bounding box around the knot. If there is clear discoloration, ensure that it is contained within the bounding box. For example, if the knot has a dark ring, ensure that the ring is entirely contained within the box.

Live Knot

Description

This a knot that belonged to a living branch. These knots may be a different color from the rest of the wood, but the boundary between them and the wood is typically softer than it is for a dead knot. The knot looks like it is part of the wood, solidly attached all the way around. If the wood has grain around the knot, the grain moves smoothly around the knot. These knots can have rings, but their boundaries are soft.

Instructions

Draw a bounding box around the knot. Some knots are clear as to where they end and the rest of the wood begins. For those knots, draw a tight bounding box. Other knots blend into the general grain of the wood. They will, however, always have a circular region where the branch grew. For those kinds of knots, do your best to draw the bounding box around the circular region and not around the rest of the warp of the grain.