UGC-YOLO: Underwater Environment Object Detection Based on YOLO with a Global Context Block

With the continuous development and utilization of marine resources, the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target detection. However, it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater environment. In this paper, a cascade model based on the UGC-YOLO network structure with high detection accuracy is proposed. The YOLOv3 convolutional neural network is employed as the baseline structure. By fusing the global semantic information between two residual stages in the parallel structure of the feature extraction network, the perception of underwater targets is improved and the detection rate of hard-to-detect underwater objects is raised. Furthermore, the deformable convolution is applied to capture longrange semantic dependencies and PPM pooling is introduced in the highest layer network for aggregating semantic information. Finally, a multi-scale weighted fusion approach is presented for learning semantic information at different scales. Experiments are conducted on an underwater test dataset and the results have demonstrated that our proposed algorithm could detect aquatic targets in complex degraded underwater images. Compared with the baseline network algorithm, the Common Objects in Context (COCO) evaluation metric has been improved by 4.34%.