基于改进YOLOv7的小目标和低对比度纸病分类算法研究

Research on Classification Method for Small Target and Low Contrast Paper Defects Based on Improved YOLOv7

关键词:  纸病分类  小目标  YOLOv7  SPPFCSPC  SimAM

Key Words:classification of paper defects  small target  YOLOv7  SPPFCPS  SimAM

摘要:随着纸机车速提升和幅宽加大，纸病出现频率随之上升。为根治纸病，需对其有效分类以溯源。但因部分纸病目标小、对比度低，分类效果欠佳。本课题提出了一种基于改进YOLOv7的分类方法，核心思想是在颈部网络改良快速跨阶段特征金字塔池化（SPPFCSPC）模块，在感受野不变前提下提升分类速度；使用空间深度卷积替换原有的“卷积+池化层”，增强对纸病的特征提取能力；通过注意力模块（SimAM），使更多的资源集中于纸病细节，进一步提高低对比度和小目标纸病的识别效率。结果表明，本课题算法的平均精度达0.97，实时检测速度26.5帧/s。相比于原YOLOv7网络，本算法在小目标和低对比度纸病的平均精度和检测速度方面均有明显提升。

Abstract:As the speed and width of the paper machine increasing， the frequency of paper defects rises accordingly. To effectively eliminate these defects， accurate classification is required for tracing their sources. However， due to the small size and low contrast of some paper defects， classification results are suboptimal. This paper proposed a classification method based on an improved YOLOv7 model. The core idea of the methodology was that the neck network improved the spatial pyramid pooling with fast cross stage partial connections （SPPFCSPC）， it improved classification speed without changing the receptive field. Space-to-depth non-strided convolution was used to replace the original “convolution + pooling layer” to enhance the feature extraction capability for paper defects. Additionally， by utilizing the similarity-based attention module （SimAM） directed more resources to paper defects details， thereby boosting recognition efficiency for low contrast and small target defects. The results showed that this algorithm attained a mean precision of 0.97 with real-time detection speed of 26.5 frames/s. Compared to YOLOv7， this method significantly improved both the mean precision and detection speed for small target and low contrast paper defects.