Development of a custom yolov5n vehicle detection algorithm using deepsort tracking system on jetson nano platform

Abstract

In recent years, the advancements in deep learning and high-performance edge-computing systems have increased tremendously and have become the center of attention when it comes to the analyzing of video-based systems on the edge by making use of computer vision techniques. Intelligent Transportation Systems (ITS) is one area where deep learning can be used for several tasks including highway-based vehicle counting systems where by making use of computer vision techniques, an edge computing device and cameras installed in specific locations on the road, we are able to obtain very accurate vehicle counting results and replace the use of traditional and laborious hardware devices with modern low-cost solutions. This thesis proposes and implements a modern, compact and reliable vehicle counting system which is based on the most recent and popular object detection algorithm as of writing this thesis known as the YOLOv5, combined with a state-of-the-art object tracking algorithm known as DeepSORT. The YOLOv5 will be used in the following system for the detection and classification of four different classes of vehicles whereas DeepSORT will be used for the tracking of those vehicles across different frames in the video sequence. Finally, a unique and efficient vehicle counting method will be implemented and used for the counting of tracked vehicles across the highway scenes. A new highway vehicle dataset consisting of four vehicle classes namely: car, motorcycle, bus and truck were collected, cleaned and annotated with a total of 11,982 images which will be published in the following study and used for the training of our robust vehicle detection model. From the results observed over real-world highway surveillance data, the following system was able to obtain an average vehicle detection mAP score of 96.1% and a vehicle counting accuracy of 95.39%, all while being able to be deployed on a compact Nvidia Jetson Nano edge computing device with an average speed of 15 FPS which outperforms other previously proposed tools in terms of both accuracy and speed.