A novel graph attention network for underwater object detection

Underwater object detection is an important computer vision task that has been widely used in marine life identification and tracking. However, problems such as low contrast conditions, occlusion condition, unbalanced light condition and small dense objects bring a series of challenges to underwater object detection. Considering these challenges, several methods have been proposed to extract features more efficiently. Attention mechanism has been proven powerful in feature extraction. However, the attention mechanism ignores the internal structure of the captured object, and conventional regular patch division is too coarse. Thus, we apply graph attention mechanisms to irregular patches and propose an Irregular-patch Graph Attention Network (IPGA). Firstly, the superpixel segmentation method is used to segment the image to reduce noise. Secondly, the global graph and local graph are constructed using clustering methods to obtain internal structures. Finally, to handle occlusion and small objects, a distinctive Feature Interaction (FIA) module is proposed to fuse information from global and local graph. To demonstrate the effectiveness of the proposed method, we conduct comprehensive evaluations on four challenging underwater datasets DUO, Brackish, TrashCan and WPBB. Experimental results demonstrate that the proposed IPGA achieves superior performance on three challenging underwater datasets.