Abstract: Aiming at the problems of low contrast of industrial defects and high false detection rate and leakage rate caused by the surrounding interference information, it proposes an industrial surface defect detection algorithm EML-YOLO based on the improvement of YOLOv8. By designing a high-efficiency large convolution module ELK, the model’s feature extraction capability can be improved by providing a multi-scale feature representation while retaining the spatial information; by proposing a parallel multi-branch feature fusion module MCM, which enables the model to acquire rich feature information and global context information; and reducing the number of parameters and computation of the model by feature compression and streamlining in the Neck module, which makes the model more applicable to industrial scenarios with limited resources. Two industrial surface defect datasets, GC10-DET and DeepPCB, are used to validate the effectiveness of the improved EML-YOLO algorithm. The experimental results show that on the GC10-DET dataset and DeepPCB dataset, the detection accuracy is improved by 4.3?percentage points and 2.9?percentage points, respectively, and the number of parametric quantities is only 2.7×106. The proposed algorithm can be better applied to industrial defect detection scenarios.

Key words: defect detection, efficient large convolution module, multi-scale features, feature compression, YOLOv8

摘要： 针对工业缺陷对比度低、周围干扰信息多导致的误检率和漏检率高的问题，提出一种基于改进YOLOv8的工业表面缺陷检测算法EML-YOLO。通过设计一种高效大卷积模块（efficient large kernel，ELK），在保留空间信息的同时提供多尺度的特征表示，从而提高模型的特征提取能力；提出多支路并行的特征融合模块（multi-scale context module，MCM），使得模型能够获取丰富的特征信息和全局上下文信息；在Neck模块中通过特征压缩和精简来减少模型的参数量和计算量，让模型更适用于资源有限的工业场景。采用GC10-DET和DeepPCB两个工业表面缺陷数据集来验证改进的EML-YOLO算法的有效性。实验结果表明，在GC10-DET数据集和DeepPCB数据集上，检测准确率上分别提高了4.3个百分点和2.9个百分点，参数量仅2.7×106。所提算法可以较好地应用于工业缺陷检测场景。

关键词: 缺陷检测, 高效大卷积模块, 多尺度特征, 特征压缩, YOLOv8