Underwater object detection in industrial recirculating aquaculture systems presents significant challenges due to low?quality captured images and fish occlusions. To address these limitations, an enhanced YOLO v10s?based underwater Atlantic salmon detection method, termed YOLO v10s?SEAM?D2S was proposed. Firstly, a separated and enhancement attention module (SEAM) was incorporated into the neck network to amplify feature responses in non?occluded regions, thereby mitigating response loss in occluded areas and improving the model?s robustness in detecting occluded targets. Secondly, a novel depth?to?space (D2S) convolution was introduced to enrich the effective information within intermediate feature maps, enhancing the model?s capability to detect objects in blurred images. Finally, grouped spatial convolution (GSConv) was integrated into the neck network to reduce computational complexity, specifically by decreasing floating point operations per second (FLOPs) and memory footprint. Experimental evaluations demonstrated that the proposed YOLO v10s?SEAM?D2S model significantly outperformed the baseline YOLO v10s on an underwater Atlantic salmon detection dataset collected in an industrial recirculating aquaculture environment. The proposed model achieved a mean average precision (mAP) of 91.6% and a precision of 84.9%, yielding respective exhibited superior robustness in detecting occluded targets. Compared with existing state?of?the?art methods, the proposed approach achieved higher detection accuracy and was particularly well?suited for underwater object detection in industrial recirculating aquaculture systems.