养殖环境中模糊、气泡遮挡等现象影响养殖鱼特征提取，使养殖鱼群检测精度不佳，为解决上述问题，提出融合通道非降维双重注意力机制ECBAM与改进YOLOv5的养殖鱼群检测模型ESB-YOLO(ECBAM-SPPF-BiFPN-YOLO)。使用ECBAM注意力机制获取更多细节特征；为缓解加入ECBAM导致的检测时间增加、速度变慢，使用SPPF替换SPP，减少模型计算量，降低模型检测时间，提高检测速度；为提高YOLOv5特征融合效果，使用BiFPN进行特征权重融合，提高有效特征在特征融合的比重，减少特征丢失。为了验证改进模块对YOLOv5的影响，设计了消融试验。试验结果显示：ESB-YOLO对比YOLOv5在保持检测速度的条件下平均精度提升了2.40%；设计模型对比试验，验证了ESB-YOLO的优越性，对比FERNet、SWIPENet与SK-YOLOv5等先进水下目标检测模型，ESB-YOLO在平均精度上分别具有3.10%、3.90%与0.70%的优势。研究表明，本研究所提模型对养殖鱼群目标检测效果更佳，可以满足养殖鱼群检测要求。

The data obtained from the breeding environment has defects such as blurring and occlusion, rendering poor detection of the farmed fish. To solve the above problem, the farmed fish detection model ESB-YOLO (ECBAM-SPPF-BiFPN-YOLO), based on a non-channel-downscaling attention mechanism (ECBAM) and improved YOLOv5, is proposed. ECBAM is adopted to obtain more detailed features. To alleviate the slowdown of detection speed caused by adopting ECBAM, SPPF is used to replace SPP to reduce model computation, decrease model detection time, and improve detection speed. BiFPN is used to perform weighted feature fusion to enhance feature fusion by increasing the proportion of functional features and reducing feature loss. To verify the effectiveness of the proposed modified module for YOLOv5, ablation tests were designed. The test results show that the average precision of ESB-YOLO is improved by 2.40% compared with YOLOv5 while maintaining a similar detection speed. To demonstrate the competitiveness of ESB-YOLO, comparison tests were designed. Compared with advanced underwater object detection models such as FERNet, SWIPENet, and SK-YOLOv5, the average precision of ESB-YOLO is 3.10%, 3.90%, and 0.70% higher, respectively. The study shows that the proposed model is more effective and capable of farmed fish detection.