Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3x3 convolution block in the E-ELAN structure, and incorporates jump connections and 1x1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. The source code for this study is publicly available at https://github.com/NZWANG/YOLOV7-AC. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks.

翻译：水下目标检测是海洋探测的关键环节。然而，传统水下目标检测方法在复杂水下环境中面临特征提取不准确、检测速度慢和鲁棒性不足等挑战。为克服这些局限，本研究提出一种改进型YOLOv7网络（YOLOv7-AC）用于水下目标检测。该网络采用ACmixBlock模块替代E-ELAN结构中的3x3卷积块，并在ACmixBlock模块间引入跳跃连接与1x1卷积架构，以提升特征提取能力与网络推理速度。同时设计ResNet-ACmix模块以避免特征信息丢失并降低计算量，在模型主干和头部部分插入全局注意力机制（GAM）以强化特征提取。此外，采用K-means++算法替代K-means算法获取锚框以提升模型精度。实验结果表明，改进型YOLOv7网络在性能上优于原始YOLOv7模型及其他主流水下目标检测方法。该网络在URPC数据集和Brackish数据集上的平均精度均值（mAP）分别达到89.6%和97.4%，且相较原始YOLOv7模型实现了更高的每秒帧数（FPS）。本研究的源代码已公开于https://github.com/NZWANG/YOLOV7-AC。综上，本研究提出的改进型YOLOv7网络为水下目标检测提供了有效的解决方案，并在各类水下任务中具有广阔的应用前景。