水下底栖生物轻量化目标检测算法YOLOv7-RFPCW  

YOLOv7-RFPCW of a lightweight target detection algorithm for benthic organisms underwater

作　　者：赵晨 陈明[1] ZHAO Chen;CHEN Ming(School of Information at Shanghai Ocean University/Key Laboratory of Fishery Information of the Ministry of Agriculture and Rural Affairs,Shanghai 201306)

机构地区：[1]上海海洋大学信息学院/农业农村部渔业信息重点实验室,上海201306

出　　处：《农业工程学报》2024年第11期168-177,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省重点领域研发计划项目(2021B0202070001)。

摘　　要：针对水下底栖生物检测模型参数量过多,计算量过大,同时水下环境复杂容易造成错检和漏检,影响模型在水下底栖生物检测任务中的准确性的问题。提出了一种水下底栖生物轻量化检测算法YOLOv7-RFPCW。对YOLOv7网络重新设计轻量级网络结构,降低了特征提取网络的参数量和计算量,减少模型体积。设计了P-ELAN和P-ELAN-W模块,进一步轻量化特征提取网络;针对水下图像颜色失真,目标的空间位置不准确的问题,采用CBAM注意力模块加强特征融合,减少信息丢失,以更好地适应特殊的水下环境;针对水下目标容易出现形状变形的问题,使用WIOU-V3损失函数替换默认的CIOU损失函数,提高水下底栖生物检测的鲁棒性。试验结果显示,改进后的模型YOLOv7-RFPCW的参数量和计算量分别减少了75.9%和30.7%,模型体积减小了75.3%,精度提升了1.9个百分点。这一综合性的提升兼顾了轻量化和精度,为在水下环境中部署提供了可行的解决方案,验证了所提出的改进算法能胜任水下底栖生物检测任务。Accurate classification and detection have posed a great challenge on benthic organisms,due to the excessive parameters,computational overhead,and complex underwater environments.In this study,a lightweight target algorithm(named YOLOv7-RFPCW)was proposed to improve the detection accuracy of underwater benthic organisms.1)The original YOLOv7 network architecture was reconstructed into the feature extraction,in order to significantly reduce the parameters and computational complexity.The overall footprint was effectively shrunk to simultaneously enhance the adaptability in the underwater scenes.This modification served as the lightweight solution.The deployment also remained computationally feasible in the resource-constrained underwater or real-time applications where processing speed was paramount.Progressive Efficient Lightweight Attention Network(P-ELAN)and its variant,P-ELAN-W,were integrated into the architecture to further lighten the network.The existing components were replaced or augmented to streamline the information flow,and then prune unnecessary computations for the essential feature representations.P-ELAN and P-ELAN-W contributed to a substantial reduction in the overall complexity.While the discriminatory power was preserved for the accurate detection of benthic organisms.A convolutional Block Attention Module(CBAM)was employed to recognize the color distortion and spatial localization in the underwater imagery.This attention mechanism was seamlessly integrated into the network to reinforce the feature fusion.The crucial visual cues were discerned in the context of the visually degraded underwater,in order to mitigate the information loss.The attention of CBAM networks was focused on the major features relevant to benthic organisms in the environmental factors of water turbidity and light scattering.2)The default CIOU loss function was substituted with the Weighted Intersection over Union-Version 3(WIOU-V3)loss function.The reason was that the shape deformation was commonly encountered with the un

关 键 词：目标 检测 深度学习 YOLOv7 底栖生物 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]