Jiang Lingjie, Yuan Baoxi, Ma Wenyun, Wang Yuqian
School of Electronic Information, Xijing University, Xi'an, China.
Shaanxi Key Laboratory of Integrated and Intelligent Navigation, The 20th Research Institute of China Electronics Technology Group Corporation, Xi'an, China.
Front Plant Sci. 2023 Jan 18;13:1108437. doi: 10.3389/fpls.2022.1108437. eCollection 2022.
Surface Defect Detection (SDD) is a significant research content in Industry 4.0 field. In the real complex industrial environment, SDD is often faced with many challenges, such as small difference between defect imaging and background, low contrast, large variation of defect scale and diverse types, and large amount of noise in defect images. Jujubes are naturally growing plants, and the appearance of the same type of surface defect can vary greatly, so it is more difficult than industrial products produced according to the prescribed process. In this paper, a ConvNeXt-based high-precision lightweight classification network JujubeNet is presented to address the practical needs of Jujube Surface Defect (JSD) classification. In the proposed method, a Multi-branching module using Depthwise separable Convolution (MDC) is designed to extract more feature information through multi-branching and substantially reduces the number of parameters in the model by using depthwise separable convolutions. What's more, in our proposed method, the Convolutional Block Attention Module (CBAM) is introduced to make the model concentrate on different classes of JSD features. The proposed JujubeNet is compared with other mainstream networks in the actual production environment. The experimental results show that the proposed JujubeNet can achieve 99.1% classification accuracy, which is significantly better than the current mainstream classification models. The FLOPS and parameters are only 30.7% and 30.6% of ConvNeXt-Tiny respectively, indicating that the model can quickly and effectively classify JSD and is of great practical value.
表面缺陷检测（SDD）是工业4.0领域的一项重要研究内容。在实际复杂的工业环境中，SDD常常面临诸多挑战，如缺陷成像与背景之间差异小、对比度低、缺陷尺度变化大且类型多样，以及缺陷图像中噪声量大等问题。枣是自然生长的植物，同一类型表面缺陷的外观差异可能很大，因此比按规定工艺生产的工业产品更难处理。本文提出了一种基于ConvNeXt的高精度轻量级分类网络JujubeNet，以满足枣表面缺陷（JSD）分类的实际需求。在所提方法中，设计了一种使用深度可分离卷积的多分支模块（MDC），通过多分支提取更多特征信息，并利用深度可分离卷积大幅减少模型中的参数数量。此外，在我们提出的方法中，引入了卷积块注意力模块（CBAM），以使模型专注于不同类别的JSD特征。在所提JujubeNet与实际生产环境中的其他主流网络进行了比较。实验结果表明，所提JujubeNet的分类准确率可达99.1%，明显优于当前主流分类模型。其FLOPS和参数分别仅为ConvNeXt-Tiny的30.7%和30.6%，表明该模型能够快速有效地对JSD进行分类，具有很大的实用价值。
