摘要
针对传统视觉方法在太阳能电池检测中面临的小目标缺陷识别准确率低、不同尺度特征捕获能力不足等问题,本文提出了一种基于跨尺度特征增强与动态参数优化的YOLOv8改进算法。首先,以多分支残差结构为核心,融合重参数化技术与可调膨胀卷积,设计膨胀重参数残差模块,通过跨层级特征交互增强目标缺陷的上下文感知能力,提高检测精度;其次,在C2f模块中嵌入可变形卷积,结合辅助检测模块构建动态特征适应网络,提升对微小缺陷的几何特征提取;最后,引入具有动态聚焦机制的损失函数优化边界框匹配,提高回归精度。实验结果表明,改进模型在3.03 M参数量下实现91.8%的平均精度,较基准模型提升4%,保持轻量参数同时提高了检测性能。
To address the challenges of low recognition accuracy for small target defects and insufficient multi-scale feature capture capability in traditional vision-based methods for solar cell inspection,this study proposes an improved YOLOv8 algorithm based on cross-scale feature enhancement and dynamic parameter optimization.First,a multi-branch residual structure is designed as the core,integrating re-parameterization techniques and adjustable dilated convolution to construct the dilation-enhanced reparametrized residual block,which enhances contextual awareness of target defects through cross-layer feature interaction,thereby improving detection accuracy.Second,deformable convolutional networks version 2 are embedded into the C2f module,combined with an auxiliary detection module to build a dynamic feature adaptation network,improving geometric feature extraction for micro-defects.Finally,a wise intersection over union loss function with a dynamic focusing mechanism is introduced to optimize bounding box matching and enhance regression precision.Experimental results demonstrate that the improved model achieves a mean average precision of 91.8%with only 3.03 M parameters,outperforming the baseline model by 4%while maintaining lightweight architecture and improved detection performance.
作者
刘闯闯
袁金丽
郑美曼
吴晨曦
郭志涛
Liu Chuangchuang;Yuan Jinli;Zheng Meiman;Wu Chenxi;Guo Zhitao(College of Electronic Information Engineering,Hebei University of Technology,Tianjin 300130,China;Innovation Research Institute,Hebei University of Technology(Shijiazhuang),Shijiazhuang 050299,China)
出处
《电子测量技术》
北大核心
2025年第21期139-147,共9页
Electronic Measurement Technology
基金
河北工业大学创新研究院(石家庄)石家庄市科技合作专项基金(SJZZXB23005,SJZZXC24011)项目资助。
关键词
光伏缺陷
YOLOv8
膨胀残差
重参数
可变形卷积
辅助检测
photovoltaic defects
YOLOv8
dilated residual
reparameterization
deformable convolution
auxiliary detection
