Underwater Object Detection Based on Improved Transformer and Attentional Supervised Fusion

Abstract

Underwater object detection is one of the important technologies for improving the efficiency of underwater inspection, but the existing methods still suffer from the problems of missed detection and insufficient target localization capability of targets. To address these problems, an improved Transformer and multi-scale attentional supervised feature fusion-based underwater object detection method is proposed. In our method, the underwater objects are preprocessed by prior knowledge first. Then, a new coordinate decomposition window-based (CDW) Transformer block is proposed to extract spatial location information more accurately, and scaling factors are introduced to reduce the intermediate computation. Finally, an attentional supervised fusion (ASF) method is proposed to strengthen the link between feature extraction and feature fusion, and further improve the detected performance by using compound attention weights. The cascade detection head is improved, where the information flow is reversed to enhance the prediction of coordinates. The average accuracy of the proposed method on the URPC and DUO datasets is 3.7% and 3.8% higher than that of the baseline network through the cross-test, and outperforms the state-of-the-art methods. This study can provide a reference for engineering applications such as automated marine operations and biodetected fishing techniques.