Shuffled Grouping Cross-Channel Attention-Based Bilateral-Filter-Interpolation Deformable ConvNet With Applications to Benthonic Organism Detection

Abstract

In this article, to holistically tackle underwater detection degradation due to unknown geometric variation arising from scale, pose, viewpoint, and occlusion under low-contrast and color-distortion circumstances, a shuffled grouping cross-channel attention-based bilateral-filter-interpolation deformable ConvNet (SGCA-BDC) framework is established for benthonic organism detection (BOD). Main contributions are as follows: 1) By comprehensively considering spatial and feature similarities between offset and integral coordinate positions, the BDC with modulation weight mechanism is created, such that sampling ability of convolutional kernel for BO with unknown geometric variation can be adaptively augmented from spatial perspective; 2) By utilizing 1-D convolution to recalibrate channel weight for grouped subfeature via information entropy statistic technique, an SGCA module is innovated, such that seabed background noise can be suppressed from channel aspect; 3) The proposed SGCA-BDC scheme is eventually built in an organic manner by incorporating BDC and SGCA modules. Comprehensive experiments and comparisons demonstrate that the SGCA-BDC scheme remarkably outperforms typical detection approaches including Faster RCNN, SSD, YOLOv6, YOLOv7, YOLOv8, RetinaNet, and CenterNet in terms of mean average precision by 8.54%, 4.4%, 5.18%, 3.1%, 3.01%, 12.53%, and 7.09%, respectively.