Underwater Species Detection using Channel Sharpening Attention

With the continuous exploration of marine resources, underwater artificial intelligent robots play an increasingly important role in the fish industry. However, the detection of underwater objects is a very challenging problem due to the irregular movement of underwater objects, the occlusion of sand and rocks, the diversity of water illumination, and the poor visibility and low color contrast in the underwater environment. In this article, we first propose a real-world underwater object detection dataset (UODD), which covers more than 3K images of the most common aquatic products. Then we propose Channel Sharpening Attention Module (CSAM) as a plug-and-play module to further fuse high-level image information, providing the network with the privilege of selecting feature maps. Fusion of original images through CSAM can improve the accuracy of detecting small and medium objects, thereby improving the overall detection accuracy. We also use Water-Net as a preprocessing method to remove the haze and color cast in complex underwater scenes, which shows a satisfactory detection result on small-sized objects. In addition, we use the class weighted loss as the training loss, which can accurately describe the relationship between classification and precision of bounding boxes of targets, and the loss function converges faster during the training process. Experimental results show that the proposed method reaches a maximum AP of 50.1%, outperforming other traditional and state-of-the-art detectors. In addition, our model only needs an average inference time of 25.4 ms per image, which is quite fast and might suit the real-time scenario.