A recent area of study focus is object detecting technologies for marine benthic organisms. A marine benthic object detection algorithm, LSH-DETR, based on DETR, is proposed to address the detection issue in the case of dense distribution of marine benthic small objects. The locality-sensitive hashing attention is used instead of self attention in the transformer to enhance the learning ability of context information. The multilevel feature pyramid networks fusion is introduced to improve the detection performance of small targets. At the same time, repulsion loss is used to optimize the prediction loss of the bounding box to improve the missing detection phenomenon caused by the target stack. According to the findings, LSH-DETR enhanced the mAP of marine benthos to 62.8% and obtained a performance of 13.8% MR when compared to the original DETR. This improvement demonstrates that LSH-DETR is more effective at locating small, dense marine benthic organisms.