An Efficient End-to-End Lightweight Object Detection Method Based on YOLOv5 for Intelligent Sweeping Robots

Abstract

This paper focuses on developing modern, efficient, lightweight object detection a method for sweeping robots while trading off parameters, FLOPs and performance. In order to comply with the real-time requirements of sweeping robots, a method combining layer pruning and channel pruning is used to compress the initial model before optimization. First, a C2f module based on shunt gradient is introduced to lighten the network. Second, a $\mathrm{C}3_{-}\mathbf{DCN}$ (Deformable convolutional networks) module is used to fit the shape and size of the object when sam-pling. Finally, a Convolutional Block Attention Module(CBAM) is added behind the backbone network to enhance the extraction of important features. In the experimental studies, we compare our method with state-of-the-art methods, and the results reveal that the new model accuracy can reach 88.5 % with only 2.27M parameters and 5.5G FLOPs. Furthermore, the experiment results show that our proposed method achieves a better balance between model size and accuracy.