Underwater Target Detection Based on Improved YOLOv7

TLDR

Underwater target detection is essential for ocean exploration, yet existing methods struggle with inaccurate feature extraction, slow speed, and limited robustness in complex environments. This study introduces an improved YOLOv7 network (YOLOv7‑AC) designed to overcome these shortcomings. YOLOv7‑AC replaces the 3×3 convolution in the E‑ELAN structure with an ACmixBlock, adds jump connections and 1×1 convolutions, incorporates a ResNet‑ACmix module, embeds a Global Attention Mechanism in the backbone and head, and uses K‑means++ for anchor box selection to enhance feature extraction and inference speed. Experimental results demonstrate that YOLOv7‑AC achieves 89.6 % mAP on the URPC dataset and 97.4 % on the Brackish dataset, surpasses the original YOLOv7 and other underwater detectors, and delivers higher FPS, indicating strong practical potential.

Abstract

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 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 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. 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.