In this study, a YOLOv5-based underwater object detection algorithm is proposed to address the challenges of mutual occlusion among underwater marine organisms, low detection accuracy for elongated objects, and presence of numerous small objects in underwater marine biological detection tasks. To redesign the backbone network and improve feature extraction capabilities, the algorithm introduced deformable convolutions, dilated convolutions, and attention mechanisms, mitigating the issues of mutual occlusion and low detection accuracy for elongated objects. Furthermore, a weighted explicit visual center feature pyramid module is proposed to address insufficient feature fusion and reduce the number of failed detections for small objects. Moreover, the network structure of YOLOv5 is adjusted to incorporate a small object detection layer that uses the fused attention mechanism, improving the detection performance for small objects. Experimental results reveal that the improved YOLOv5 algorithm achieves a mean average precision of 87.8% on the URPC dataset, demonstrating a 5.3 percentage points improvement over the original YOLOv5 algorithm while retaining a detection speed of 34 frame/s. The proposed algorithm effectively improves precision and reduces missed and false detection rates in underwater object detection tasks.