  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.
