Abstract
Crayfish sub-part processing requires high precision and efficiency in complex environments; however, current methods often fail to effectively handle small anatomical structures and are unsuitable for edge-device deployment. We propose YOLOv11nDHBC, a lightweight detection and segmentation framework that integrates three technical innovations. First, the proposed D-HGNetV2 backbone integrates DynamicConv modules into the HGNetV2 architecture, reducing parameters by 31% (from 2.9 M to 2.0 M) and computational cost by 9.8% (10.2 GFLOPs to 9.2 GFLOPs) through input-dependent kernel aggregation, which enhances multiscale feature extraction for occluded or overlapping parts. Second, a bidirectional feature pyramid network (BiFPN) with learnable weights adaptively fuses crossscale representations, strengthening finegrained detail capture in cluttered environments while controlling computational overhead. Third, the CARAFE contentaware upsampling module replaces nearestneighbor interpolation to preserve highresolution information, boosting smalltarget segmentation (e.g., claws and legs) without significant model size growth. On our selfconstructed dataset, YOLOv11nDHBC achieves 96.8% detection mAP@0.5 and 96.0% segmentation mAP@0.5-surpassing the YOLOv11nSeg baseline-while maintaining realtime inference at 65.8 FPS and a model size of 4.2 MB (26.3% smaller). This balanced design offers robust performance for automated segmentation of crayfish sub-parts, thereby facilitating deployment in aquatic processing systems.