A lightweight fish object detection method improved based on the YOLOv7 model
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摘要: 为了解决商业渔船电子监控系统中鱼类检测和识别依赖于人工完成的问题，提出一种基于YOLOv7的轻量级鱼类实时检测模型YOLOv7-MRN，将YOLOv7的骨干网络替换为MobileNetv3骨干网络，以降低运算量，并添加了感受野模块RFB来增强网络的特征提取能力；通过引入基于归一化的注意力机制模块NAM，重新设计颈部特征融合网络，以抑制无关紧要的权重。结果表明：在HNY768远洋渔船电子监控视频渔业数据集上，YOLOv7-MRN模型的mAP@0.5为86.5%，运算量仅为原模型YOLOv7的9.8%，模型在GPU和CPU上的推理速度分别提高了121.69%和219.09%；相较于其他模型，YOLOv7-MRN模型的实际检测效果更好，尤其是在强日光场景下。研究表明，本文中提出的YOLOv7-MRN模型对鱼类的检测效果好，消耗的计算资源更少，可将该模型部署在电子渔船监控系统中。
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关键词:
- YOLOv7 /
- 基于归一化的注意力机制 /
- 深度可分离卷积 /
- 鱼类目标检测
Abstract: To address the reliance on manual labor for fish detection and recognition in commercial fishing vessel electronic monitoring systems, a lightweight real-time fish detection model called YOLOv7-MRN was proposed in which the backbone network of YOLOv7 was replaced with the MobileNetv3 backbone network to reduce computational complexity. Additionally, receptive field modules were incorporated to enhance the network's feature extraction capabilities. The neck feature fusion network RFB to suppress irrelevant weights was redesign by introducing a normalization-based attention mechanism module NAM. The test of the HNY768 offshore fishing vessel electronic monitoring video fishery dataset revealed that YOLOv7-MRN achieved mAP@0.5 of 86.5%, with only 9.8% of the computational load compared to the original model. The inference speed of the model was improved by 121.69% on GPU and 219.09% on CPU. In comparison to other models, YOLOv7-MRN exhibited superior performance in practical fish detection, particularly in strong sunlight conditions. These findings indicate that the YOLOv7-MRN model proposed here can be deployed in electronic fishing vessel monitoring systems with reduced computational resource consumption to accomplish fish detection tasks. -
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