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.