Detection of Weak and Small Targets in Forward-Looking Sonar Image Using Multi-Branch Shuttle Neural Network

Abstract

Autonomous Underwater Vehicle (AUV) is an automated navigation device, which can independently complete the underwater investigation and detection without supervision. However, the marine environment is getting increasingly severe, and the garbage in the ocean and fishnets cast by fishermen have a great negative influence on the work of AUV. To solve the problems of low precision and slow speed in the state-of-the-art network detection of weak and small targets in underwater sonar images, Yolo5 was improved by building the multi-branch shuttle neural network The dataset is collected by multi-beam forward-looking sonar Gemini720i, and includes “fishnet” and two representative types of knitted and plastic garbage, i.e., “cloth” and “plasticbag”. The original dataset is enhanced and balanced, and the effect of dataset distribution on the model performance is studied. Utilizing pre-training, the effects of Yolo5 family, i.e., Yolo5s, Yolo5m, Yolo5l and Yolo5x are compared using the balanced dataset, to explore the impacts on detecting weak and small targets of the deeper and wider networks of this family. In addition, the combinations of Yolo5s with lightweight networks, i.e., MobileNet3 and ShuffleNet2 are experimented, which further illustrates the effectiveness of the proposed network, and can satisfy the high accuracy and real-time requirements of AUV.