Object Detection Algorithm Evaluation in the Context of Indian Transportation

Computer vision plays a crucial role in detecting objects. Has various applications, including traffic management and autonomous vehicles. This study aims to evaluate the performance of different object identification algorithms in the context of transportation in India. The unique traffic patterns, diverse vehicle types, and infrastructure challenges make it an interesting and demanding scenario for object detection systems. Our research focuses on assessing the precision, robustness, and suitability of state of the art object recognition algorithms like YOLO V6, YOLO V7, YOLO V8 and Faster R-CNN in identifying and categorizing objects commonly seen in traffic scenes. The findings from this study provide insights for developing transportation systems specifically designed for the Indian environment. By understanding the strengths and weaknesses of algorithms when dealing with traffic situations, varying lighting conditions and a wide range of vehicle types this research contributes to advancing computer vision technology for transportation, in India.