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Target detection research has always been difficult when it comes to small target detection in underwater situations. To address the issues of a high miss detection rate and poor underwater scene recognition in underwater small target detection tasks, an improved underwater small target detection technique utilizing YOLOv7 is proposed. To achieve the accuracy rate while considering the high detection speed, the YOLOv7 network is used as the basic network. The network concentrates more crucial feature information of small targets to increase detection accuracy while reducing model complexity by merging the SENet attention mechanism, enhancing the FPN network topology, and incorporating the EIoU loss function. Through simulation tests, the mAP, P, and R metrics are confirmed on the test set and contrasted with other conventional target detection techniques. The outcomes demonstrate that the enhanced algorithm outperforms competing networks and successfully raises detection accuracy on the test set.