Car Crash Dataset (CCD) is collected for traffic accident analysis. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions), whether ego-vehicles involved, accident participants, and accident reason descriptions.

CCD Camera Market size was valued at USD 24.4 Billion in 2024 and is projected to reach USD 49.35 Billion by 2031, growing at a CAGR of 6.9% during the forecasted period 2024 to 2031.

Global CCD Camera Market Drivers

The market drivers for the CCD Camera Market can be influenced by various factors. These may include:

Demands for Quality and Resolution: CCD cameras, which are renowned for their exceptional image quality, particularly in low light, are always in demand as more industries, including astronomy, automotive, healthcare, and surveillance, need higher image quality and resolution for a variety of applications. Applications for Security and Surveillance: As security concerns develop, so does the necessity for surveillance systems in public areas, businesses, and residential areas. For surveillance applications, CCD cameras are recommended because of their superior sensitivity and low-light capabilities. Industrial Applications: CCD cameras are widely used in industrial settings for quality control, inspection, and machine vision. CCD cameras are essential for precise imaging and inspection work in industries such as manufacturing, electronics, and automotive. Medical Imaging: CCD cameras are employed in radiography, endoscopy, microscopy, and ophthalmology, among other imaging-related fields in the medical industry. The market for CCD cameras is still being driven by the need for high-quality medical imaging equipment. Astronomy & Scientific study: To obtain high-resolution pictures of celestial objects and processes, CCD cameras are extensively employed in astronomy and scientific study. Better sensitivity and resolution CCD cameras are still needed as astronomy and research continue to improve. Automotive Vision Systems: With the increasing deployment of advanced driver assistance systems (ADAS) and driverless vehicles, the demand for CCD cameras in automotive vision systems is growing. Applications including lane departure warning, accident avoidance, and parking assistance employ CCD cameras. Consumer electronics: Despite CMOS (Complementary Metal-Oxide-Semiconductor) sensors' dominance in the consumer camera market due to their affordability and power efficiency, CCD cameras continue to find niche uses in high-end devices like digital cameras and camcorders, particularly for image quality-aware professionals and enthusiasts. Non-Consumer Photography and Videography: Professionals in domains including photography, filmmaking, and videography choose CCD cameras due to its exceptional dynamic range, colour accuracy, and image quality, especially in studio and controlled lighting situations.

The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.