To address the problems of insufficient automatic species detection accuracy and difficulty in edge deployment caused by complex deck backgrounds， illumination variations， fish occlusion， and fine-grained differences among similar species in shipborne camera images of tuna targets in distant-water fisheries， a lightweight tuna species detection algorithm based on YOLO11n， named ACLB-YOLO， is proposed. YOLO11n is used as the baseline model. In the backbone network， the multi-cognitive visual adapter Mona is integrated with C2PSA to enhance robust feature representation under strong illumination， local overexposure， and complex backgrounds. In the neck network， a BiFPN-GLSA module is constructed to strengthen feature association for densely occluded targets through a global-local spatial attention mechanism. In the detection head， the Detect_AFPN_P345 progressive feature pyramid is introduced to alleviate semantic conflicts in multi-scale feature fusion. Furthermore， layer-adaptive magnitude-based pruning （LAMP） is adopted for lightweight model compression， and embedded deployment verification is conducted on the NVIDIA Jetson Orin NX platform. The results show that， on the self-built tuna dataset， the pruned ACLB-YOLO achieves an mAP@0.5 of 86.15%， which is 9.03 percentage points higher than that of the YOLO11n baseline model， while the model size is reduced from 5.1 MB to 2.4 MB. On the embedded platform， the pruned model achieves an average inference speed of 46.5 FPS and a total latency of 21.51 ms per frame. ACLB-YOLO can effectively improve tuna species detection performance in complex shipborne image scenarios， and the pruned model significantly reduces model complexity while maintaining good detection accuracy. This study can provide technical support for automatic tuna classification， catch statistics， and real-time deployment on shipborne edge devices in electronic monitoring systems for distant-water fisheries.