标题： 基于轻量级卷积优化的钢表面缺陷检测方法 显示英文标题

标题： A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization

摘要： 钢铁表面缺陷检测，特别是多尺度缺陷的识别，一直是工业制造中的重大挑战。 钢铁表面不仅存在各种大小和形状的缺陷，这限制了传统图像处理和检测方法在复杂环境中的准确性。 然而，传统的缺陷检测方法在处理小目标缺陷时面临准确率不足和误检率高的问题。 为了解决这个问题，本研究提出了一种基于深度学习的检测框架，具体为结合C3Ghost模块、SCConv模块和CARAFE上采样算子的YOLOv9s，以提高检测准确性和模型性能。 首先，SCConv模块通过重构空间和通道维度来减少特征冗余并优化特征表示。 其次，引入C3Ghost模块通过减少冗余计算和参数量来增强模型的特征提取能力，从而提高模型效率。 最后，CARAFE上采样算子可以以内容感知的方式更精细地重新组织特征图，优化上采样过程并确保高分辨率缺陷区域的详细恢复。 实验结果表明，与其它方法相比，所提出的模型在钢铁表面缺陷检测任务中实现了更高的准确性和鲁棒性，有效解决了缺陷检测问题。 显示英文摘要

摘要： Surface defect detection of steel, especially the recognition of multi-scale defects, has always been a major challenge in industrial manufacturing. Steel surfaces not only have defects of various sizes and shapes, which limit the accuracy of traditional image processing and detection methods in complex environments. However, traditional defect detection methods face issues of insufficient accuracy and high miss-detection rates when dealing with small target defects. To address this issue, this study proposes a detection framework based on deep learning, specifically YOLOv9s, combined with the C3Ghost module, SCConv module, and CARAFE upsampling operator, to improve detection accuracy and model performance. First, the SCConv module is used to reduce feature redundancy and optimize feature representation by reconstructing the spatial and channel dimensions. Second, the C3Ghost module is introduced to enhance the model's feature extraction ability by reducing redundant computations and parameter volume, thereby improving model efficiency. Finally, the CARAFE upsampling operator, which can more finely reorganize feature maps in a content-aware manner, optimizes the upsampling process and ensures detailed restoration of high-resolution defect regions. Experimental results demonstrate that the proposed model achieves higher accuracy and robustness in steel surface defect detection tasks compared to other methods, effectively addressing defect detection problems.