Feature Fusion Network with Local Information Exchange for Underwater Object Detection

When using enhanced images for underwater object detection, issues such as detail loss and increased noise often arise, leading to decreased detection efficiency. To address this issue, we propose the Feature Fusion Network with Local Information Exchange (FFNLIE) for underwater object detection. We input raw and enhanced images into the Swin Transformer in parallel for feature extraction. Then, we propose a local information exchange module to enhance the feature extraction capability of the Swin Transformer. In order to fully utilize the complementary information of the two images, our feature fusion module consists of two core components: the Discrepancy Information Addition Block (DIAB) and the Common Information Addition Block (CIAB). The DIAB and CIAB are designed by utilizing and modifying cross-attention mechanisms, which can easily extract image discrepancy information and common information. Finally, the fused features are fed into the object detector to perform object detection tasks. The experimental findings demonstrate that the FFNLIE exhibited exceptional performance across four underwater datasets.