CWSCNet: Channel-Weighted Skip Connection Network for Underwater Object Detection

Autonomous underwater vehicles (AUVs) equipped with the intelligent underwater object detection technique is of great significance for underwater navigation. Advanced underwater object detection frameworks adopt skip connections to enhance the feature representation which further boosts the detection precision. However, we reveal two limitations of standard skip connections: (1) standard skip connections do not consider the feature heterogeneity, resulting in a sub-optimal feature fusion strategy; (2) feature redundancy exists in the skip connected features that not all the channels in the fused feature maps are equally important, the network learning should focus on the informative channels rather than the redundant ones. In this paper, we propose a novel channel-weighted skip connection network (CWSCNet) to learn multiple hyper fusion features for improving multi-scale underwater object detection. In CWSCNet, a novel feature fusion module, named channel-weighted skip connection (CWSC), is proposed to adaptively adjust the importance of different channels during feature fusion. The CWSC module removes feature heterogeneity that strengthens the compatibility of different feature maps, it also works as an effective feature selection strategy that enables CWSCNet to focus on learning channels with more object-related information. Extensive experiments on three underwater object detection datasets RUOD, URPC2017 and URPC2018 show that the proposed CWSCNet achieves comparable or state-of-the-art performances in underwater object detection.