LF-YOLO for Strip Surface Defect Detection in Industrial Scenes

doi:10.3778/j.issn.1002-8331.2404-0411

Abstract

Abstract: Aiming at the problem of low accuracy of traditional defect detection algorithms in practical applications due to the small size of strip surface defects and blurry collected images in industrial scenarios, an LF-YOLO algorithm for strip surface defect detection in industrial scenarios is proposed. The model upsamples the input pixels by designing a local filling upsampling module to improve the recognition ability of blurred images, and reduce the missed detection rate of small target defects. The FReLU activation function that focuses on visual tasks is introduced to improve the accuracy of model location defects. In addition, a lightweight local attention mechanism is proposed and combined with the feature extraction module C2f to enhance the feature extraction capability of defects of different sizes during the feature extraction process of the model. Experimental results on the Northeastern University open source strip steel dataset NEU-DET and GC10-DET show that the average detection accuracy of the improved model is 7.0 and 15.4 percentage points higher than the accuracy of the original YOLOv8 algorithm, and is better than other classic target detection models. It has advantages in average detection accuracy, and the validity of each module is further verified through ablation experiments.

Key words: strip surface defect detection, deep learning, upsampling, attention mechanism, activation function

摘要： 针对工业场景下带钢表面缺陷尺寸大小不一、采集图像模糊导致传统缺陷检测算法在实际应用中精度低的问题，提出一种面向工业场景带钢表面缺陷检测的LF-YOLO算法。模型通过设计一种局部填充上采样模块对输入像素进行上采样，提高模型对模糊图片的识别能力，降低模型对小目标缺陷的漏检率。通过引入专注视觉任务的FReLU激活函数，提高模型定位缺陷的准确率。提出一种轻量级的漏斗注意力机制并与特征提取模块C2f进行结合，增强模型对不同尺寸缺陷的特征提取能力。在开源数据集NEU-DET与GC10-DET上的实验结果表明，改进后的模型平均检测精度比原始YOLOv8算法精度分别高7.0和15.4个百分点，且相较于其他目标缺陷检测模型在平均检测精度方面具有优势，并进一步通过消融实验验证了每个模块的有效性。

关键词: 带钢表面缺陷检测, 深度学习, 上采样, 注意力机制, 激活函数

MA Xiaoyao, LI Rui, LI Zili, ZHAI Wenzheng. LF-YOLO for Strip Surface Defect Detection in Industrial Scenes[J]. Computer Engineering and Applications, 2024, 60(18): 78-87.

马肖瑶, 黎睿, 李自力, 翟文正. 面向工业场景带钢表面缺陷检测的LF-YOLO[J]. 计算机工程与应用, 2024, 60(18): 78-87.

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