MBR-YOLO: efficient and precise object detection algorithm in marine benthos

Marine benthic organism detection is a critical field in underwater object detection. Due to the small size, diverse morphology, and occlusion of marine benthic organisms, general object detection algorithms struggle to achieve high accuracy in this domain. To address these challenges, we propose the multi-scale feature extraction and reinforced bounding box regression for the marine benthos detection (MBR-YOLO) algorithm. The key innovations of MBR-YOLO are as follows: First, we introduce the cross-stage multi-scale aggregation inception module, which uses multi-scale convolutions to extract features and parallel branches to fuse contextual information. This enhances the model’s sensitivity to small object features and mitigates the problem of insufficient feature extraction for small targets. Second, we propose a three-dimensional group convolution channel shuffle feature pyramid network, which combines features of different scales. This structure employs a feature extraction mechanism to automatically capture scale information and leverages channel transformation to reorganize multi-scale details. This improves the model’s ability to perceive multi-scale targets and maintain invariance in fine details. Finally, we design minimal geometry distance Intersection over Union (MGDIoU) and MGDIoU-non-maximum suppression to address occlusion issues. MGDIoU redefines the minimum geometric diagonal distance metric, enhancing Intersection over Union computation. Using MGDIoU as a guiding strategy for non-maximum suppression preserves more predictions closer to the ground truth, reducing missed detections. Experiments demonstrate that MBR-YOLO improves mAP0.5:0.95 by 4.8%, 5.5%, and 5.5% on the Underwater Robot Picking Contest 2020 (URPC2020), SUODAC, and AUDD datasets, respectively. Compared with 13 state-of-the-art detection algorithms, including YOLOv10, MBR-YOLO achieves higher accuracy. MBR-YOLO provides robust data support for marine benthic detection and offers a theoretical reference for future research in related fields.