Vehicle and Pedestrian Detection Algorithm Based on Lightweight YOLOv3-Promote and Semi-Precision Acceleration

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 10; pp. 19760 - 19771
• Main Authors: Xu, He, Guo, Mingtao, Nedjah, Nadia, Zhang, Jindan, Li, Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithms; Computational modeling; Computer networks; Convolution; Feature extraction; Kernel; Lightweight; lightweight model; model deployment; model prune; Object detection; Real-time systems; Scaling factors; semi-precision acceleration; Target detection; Telecommunications
• ISSN: 1524-9050; 1558-0016; 1558-0016
• DOI: 10.1109/TITS.2021.3137253

Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model's to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA's TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.