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:水下目标探测是海洋勘探的一个重要方面。然而,传统的水下目标探测方法面临若干挑战,例如不准确的地物提取、缓慢的探测速度和复杂的水下环境中缺乏稳健性。为解决这些局限性,本研究建议改进YOLOv7网络(YOLOv7-AC),用于水下目标探测。拟议的网络使用ACmixBlock模块取代E-ELAN结构中的3x3 convolution块,并纳入AcmixBlock模块之间的跳动连接和1x1 convolution架构,以提高地物提取速度和网络推力速度。此外,ResNet-ACmix模块旨在避免地物信息丢失和减少计算,同时在模型的骨干和头部分插入全球关注机制(GAM),以改进地貌提取。此外,使用K-mexBUMS+LA算法取代K的锚框,提高模型改进的YOLOV7模型,改进后的YOLVV7模型和其他受欢迎的水下目标探测方法。拟议的网络实现了平均精确度,而BROLO4的原始数据框架则显示为:89-ROLVS的原始的原始数据。