VigilDrive is a computer vision–powered driver monitoring system designed to detect signs of fatigue and drowsiness in real-time. It uses a Raspberry Pi 5 paired with a camera module to analyze the driver's face. The core objective is to reduce the risk of drowsy driving incidents by providing early warnings based on facial cues such as eye closure, yawning, and head tilting.

The project uses a YOLO-based object detection model trained on a custom dataset labeled with key fatigue-related classes.

This project uses a Raspberry Pi equipped with the official Pi Camera and a custom-trained YOLOv8 nano model to detect crickets in real time. It leverages picamera2, OpenCV, and the ultralytics library to capture video frames, run inference, and display bounding boxes over detected insects. After detection, a mechanical flipper arm—similar to those found in pinball machines—is triggered to sort the crickets into two separate boxes based on their predicted sex (male or female).

AI-Powered Piano Trainer

This project uses computer vision and a custom-trained object detection model to detect which piano keys are pressed. It compares them to the correct sequence of a song and gives the user an accuracy score at the end. It runs on a Raspberry Pi and laptop, with a camera, LCD screen, and buzzer for interaction.

The Insect Detect - insect classification dataset v2 contains mainly images of various insects sitting on or flying above an artificial flower platform. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (bioRxiv preprint). Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the first dataset version were transferred to this dataset. This dataset is also available on Roboflow Universe. The images in the dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use this uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided training notebook.

Classes This dataset contains the following 27 classes:

ant (Formicidae) bee (Anthophila excluding Apis mellifera and Bombus sp.) bee_apis (Apis mellifera) bee_bombus (Bombus sp.) beetle (Coleoptera excluding Coccinellidae and some Oedemeridae) beetle_cocci (Coccinellidae) beetle_oedem (visually distinct Oedemeridae) bug (Heteroptera excluding Graphosoma italicum) bug_grapho (Graphosoma italicum) fly (Brachycera excluding Empididae, Sarcophagidae, Syrphidae and small Brachycera) fly_empi (Empididae) fly_sarco (visually distinct Sarcophagidae) fly_small (small Brachycera) hfly_episyr (hoverfly Episyrphus balteatus) hfly_eristal (hoverfly Eristalis sp., mainly Eristalis tenax) hfly_eupeo (mainly hoverfly Eupeodes corollae and Scaeva pyrastri) hfly_myathr (hoverfly Myathropa florea) hfly_sphaero (hoverfly Sphaerophoria sp., mainly Sphaerophoria scripta) hfly_syrphus (mainly hoverfly Syrphus sp.) lepi (Lepidoptera) none_bg (images with no insect - background (platform)) none_bird (images with no insect - bird sitting on platform) none_dirt (images with no insect - leaves and other plant material, bird droppings) none_shadow (images with no insect - shadows of insects or surrounding plants) other (other Arthropods, including various Hymenoptera and Symphyta, Diptera, Orthoptera, Auchenorrhyncha, Neuroptera, Araneae) scorpionfly (Panorpa sp.) wasp (mainly Vespula sp. and Polistes dominula) For the classes hfly_eupeo and hfly_syrphus a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that are important for a classification into one of these classes, some specimens of Syrphus sp. might look more like Eupeodes sp. and vice versa. The images were sorted to the respective class by considering taxonomic and visual distinctions. However, this dataset is still rather small regarding the visually extremely diverse Insecta. Insects that are not included in this dataset can therefore be classified to the wrong class. All results should always be manually validated and false classifications can be used to extend this basic dataset and retrain your custom classification model.

Deployment You can use this dataset as starting point to train your own insect classification models with the provided Google Colab training notebook. Read the model training instructions for more information. A insect classification model trained on this dataset is available in the insect-detect-ml GitHub repo. To deploy the model on your PC (ONNX format for fast CPU inference), follow the provided step-by-step instructions.

License This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).