Research on Vehicle Detection and Tracking Algorithm Based on YOLOv8 and ByteTrack
The detection and tracking of moving vehicles is one of the key technologies of intelli-gent transportation system,and the traditional vehicle detection and tracking methods have poor real-time performance,easy to be disturbed by the background environment,and the similarity of vehicle morphology leads to vehicle misdetection.In order to solve this problem,a vehicle de-tection and tracking algorithm based on YOLOv8 and ByteTrack is proposed.In the vehicle de-tection stage,for the problems of complex model structure and large computation,the backbone network of YOLOv8 is replaced by a lightweight network MobileNetV3 to reduce the number of parameters and computation of the model and ensure the real-time vehicle detection and tracking;for the problems of vehicle misdetection during camera shooting,the YOLOv8 detec-tion head is replaced by the DyHead dynamic target detection head,which can recognize the tar-get vehicle more accurately.Finally,the improved YOLOv8 detection algorithm and ByteTrack tracking algorithm are combined to complete the multi-target vehicle tracking,and it is proved by the experiments that the number of parameters is reduced by 45.0％ and the amount of com-putation is reduced by 46.3％ while the accuracy is almost unchanged,which proves the effec-tiveness of the algorithm,and the improved model has a better real-time performance and track-ing accuracy,which can satisfy the practical use requirements.
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