Real-Time Object Detection Methods for Road Scenarios

Wildlife on public roads presents a public safety problem as well as an environmental concern. The field of object detection within Computer Vision offers some approaches that can be used either in autonomous vehicles or within emergency braking systems for collision avoidance. This paper aims to study how a popular and efficient object detection model and a keypoint-based model compare against each other on an SoC specialized for Computer Vision tasks which is used in automotive systems. Insights into how these object detection models perform on resource-constrained hardware and into the importance of the overall approach are gained while also highlighting the advantages and disadvantages of each method and the hindrances of deploying such algorithms on hardware. While well-established models like YOLO outpace the implemented keypoint-based approach in both precision and speed, the total runtime of the two models is ultimately close, with the biggest takeaway being that the robust YOLO model also requires extensive post-processing, which is computationally expensive, especially on an embedded SoC.