关键词: 深度学习, 卷积神经网络, MobileNet, YOLOv3, 轻量化网络

Abstract: Aiming at the problems of poor compatibility，low computational power and excessive memory occupied in the training process of current convolutional neural network-based target detection algorithms in small image processing computing platform，a ligntweighted convolutional neural network named YOLO-Slim was proposed and its feasibility was verified by YOLOv3. Firstly，the network parameters and computational complexity were greatly reduced by changing the network infrastructure and replacing the standard convolution with the deep separable convolution. Secondly，according to the impact of network layer on mAP（mean Average Precison），the network layer was pruned to achieve inter-layer pruning of network；then the median channel pruning strategy was used to achieve intra-layer pruning of network；finally，the lightweight network design was completed. The experimental results show that the YOLO-Slim designed on VOC2007 test data set is 90% smaller than the original YOLOv3 in model size，76. 42% in mAP and 16 ms in recognition speed. It can provide fast and accurate target recognition capability for small image computing platform.

Key words: deep learning, Convolutional Neural Network (CNN), MobileNet, YOLOv3, lightweight network