A Lightweight Sea Surface Object Detection Network for Unmanned Surface Vehicles

Abstract

For unmanned surface vehicles (USVs), perception and control are commonly performed in embedded devices with limited computing power. Sea surface object detection can provide sufficient information for USVs, while most algorithms have poor real-time performance on embedded devices. To achieve real-time object detection on the USV platform, this paper designs a lightweight object detection network based on YOLO v5. In our work, an improved ShuffleNet v2 based on the attention mechanism was adopted as a backbone network to extract features. The depth-wise separable convolution module was introduced to rebuild the neck network. Additionally, the fusion method was changed from Concat to Add to optimize the feature fusion module. Experiments show that the proposed method reached 32.64 frames per second (FPS) on the Nvidia Jetson AGX Xavier and achieved a mean average precision (mAP) of 93.1% and 93.9% on our dataset and Singapore Maritime Dataset, respectively. Moreover, the number of model parameters of the proposed network was only 25% of that of YOLO v5n. The proposed network achieves a better balance between speed and accuracy, which is more suitable for detecting sea surface objects for USVs.