LI Jing,XU Zhengjun,XU Liang.Vehicle and pedestrian detection method based on improved YOLOv4-tiny[J].Optoelectronics Letters,2023,(10):623-628 Vehicle and pedestrian detection method based on improved YOLOv4-tiny Author Name Affiliation LI Jing  School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China  XU Zhengjun  School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China  XU Liang  School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China  Abstract:       Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models, this paper proposes an improved you only look once v4 (YOLOv4)-tiny vehicle and pedestrian target detection algorithm. Convolutional block attention module (CBAM) is introduced into cross stage partial Darknet-53 (CSPDarknet53)-tiny module to enhance feature extraction capabilities. In addition, the cross stage partial dense block layer (CSP-DBL) module is used to replace the original simple convolutional module superposition, which compensates for the high-resolution characteristic information and further improves the detection accuracy of the network. Finally, the test results on the BDD100K traffic dataset show that the mean average precision (mAP) value of the final network of the proposed method is 88.74%, and the detection speed reaches 63 frames per second (FPS), which improves the detection accuracy of the network and meets the real-time detection speed. Hits: 232 Download times: 0 View Full Text    Download reader