A Geometry-Aware Framework with Dynamic Edge Relation Modeling for Underwater Fish Perception and Non-Contact Body Size Estimation

Accurate underwater fish perception is a prerequisite for intelligent aquaculture, providing essential support for automated growth monitoring and precision feeding. Nevertheless, underwater imaging is severely affected by light scattering and cluttered backgrounds, making reliable fish perception and image-based non-contact fish size estimation particularly challenging. To overcome these limitations, this paper presents a geometry-aware underwater fish perception framework based on YOLOv5 for simultaneous object detection, pose estimation, and fish body size measurement. The proposed framework incorporates a Fish Geometry Coordinate Attention (FGCA) module into the backbone to enhance geometric feature representation by jointly modeling horizontal and vertical spatial dependencies, thereby improving the discrimination of elongated fish structures. In addition, a Dynamic Edge Relation Modeling (DERM) module is integrated into the neck to explicitly capture edge cues and structural relationships among key body regions, leading to more robust feature aggregation and more accurate key point localization under complex underwater conditions. Based on the predicted key points, the length and width of the fish can be estimated in a non-contact manner. Experimental results on a public underwater fish dataset demonstrate that the proposed framework achieves a Box mAP0.5 of 93.07% and a Pose mAP0.5 of 93.10%, consistently outperforming several representative YOLO-based detectors. Moreover, the proposed method maintains low body size estimation errors across different fish size categories. These results indicate that the proposed framework provides an accurate and practical solution for integrated underwater fish perception and image-based non-contact fish size estimation, offering significant potential for intelligent aquaculture and automated fish growth monitoring.