TTD-YOLO: A Real-Time Traffic Target Detection Algorithm Based on YOLOV5

Abstract

To solve the problems of limited computing power resources, low accuracy of small target detection, high miss rate, and poor real-time detection of mobile vehicle platforms in the automatic driving environment, The present study introduced a one-stage target detection algorithm TTD-YOLO (Traffic Target Detection YOLO) that improved YOLOV5-S, which is enhanced in four aspects: Enhanced the network’s multi-scale feature extraction performance through the utilization of the improved M-ELAN architecture; added 3D attention mechanism SimAM to the network structure to enable the network to learn important feature information and enhance the efficiency of detecting accuracy; the parameter ratio of backbone and neck is adjusted to close to 1:1 by adjusting the number of output channels and stacking times of CSPLayer modules in the backbone and neck, while maintaining the model complexity, experiments show that improving the neck’s parameter ratio helps enhance the efficiency of detecting accuracy without changing the network structure; used EIoU loss instead of the bounding box loss function accelerates network convergence and improved detection accuracy. Under the condition of avoiding significantly changing the network structure, our TTD-YOLO outperforms the baseline model and other mainstream object detection algorithms such as Faster RCNN, SSD, and YOLOX-S on the autopilot dataset SODA10M with fewer parameters, higher detection accuracy, and faster inference speed. Compared to the baseline model, the model parameters decreased by 8.6%, average precision(mAP@.5:.95)increased by 2.5%, and the inference speed under the same experimental platform increased by 4.8%.