In aquaculture， target detection serves as a fundamental basis for monitoring the behavior and evaluating the growth status of cultured organisms. However， the complexity of the underwater environment often results in degraded image quality， and the tendency of organisms to aggregate further complicates detection， leading to relatively low accuracy in target identification. To address these issues， this paper proposes an improved aquaculture target detection algorithm based on YOLO11n. A lightweight network StarNet is introduced into the backbone network to reduce the number of model parameters and computation load. Mixed Aggregation Network （MANet） is adopted in the neck network to perform multi-scale fusion on aquaculture targets and mitigate the detection deviation caused by blurred images. A Separated and Enhancement Attention Module （SEAM） is incorporated into the detection head to improve the model's detection accuracy in scenarios with biological aggregation and complex backgrounds. The Wise-MPDIoU （Wise Modified Penalized Distance Intersection over Union） loss function is adopted to replace the original one， so as to enhance the robustness of aquaculture organism detection. Experimental results demonstrate that， on the Underwater Target Detection and Classification 2020 （UTDAC2020） and Brackish datasets， the parameter count of the improved YOLO11n model was reduced by 16%. Meanwhile， the precision was improved by 1.1% and 0.2% respectively， the recall was increased by 2.8% and 0.4% respectively， and the mean average precision was elevated by 2.5% and 0.5% respectively. This model achieves high detection accuracy while ensuring lightweight design， and has been successfully deployed on hardware devices equipped with entry-level consumer-grade graphics cards， thus providing a reliable solution for aquaculture target detection tasks.