| 赵佰亭,张晨,贾晓芬.ECC-YOLO：一种改进的钢材表面缺陷检测方法[J].电子测量与仪器学报,2024,38(4):108-116 |
| ECC-YOLO：一种改进的钢材表面缺陷检测方法 |
| ECC-YOLO: An improved method for detecting surface defects in steel |
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| DOI： |
| 中文关键词: 目标检测 缺陷检测 YOLOv7 ConvNeXt 注意力机制 |
| 英文关键词:target detection defect detection YOLOv7 ConvNeXt attention mechanism |
| 基金项目:国家自然科学基金面上项目（52174141）、安徽省自然科学基金面上项目（2108085ME158）资助 |
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| Author | Institution |
| Zhao Baiting | China Institute of Electrical and Information Engineering, Anhui University of Science and Technology,
Huainan 232001, China |
| Zhang Chen | China Institute of Electrical and Information Engineering, Anhui University of Science and Technology,
Huainan 232001, China |
| Jia Xiaofen | 1.China Institute of Electrical and Information Engineering, Anhui University of Science and Technology,
Huainan 232001, China; 2.State Key Laboratory of Mining Response and Disaster Prevention and Control
in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China |
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| 中文摘要: |
| 针对当前钢材表面缺陷检测效率低、检测精度差的问题，提出了一个模型，命名为ECC-YOLO，基于YOLOv7的钢材表面缺陷检测。首先，为了提高主干网络特征图信息表征能力，引入了特征增强模块ConvNeXt，通过融合深度可分离卷积、大核卷积，增强模型对细小裂缝的特征提取能力，其次设计了C2fFB模块，在增强目标特征信息的提取能力同时，显著降低了模型的计算量和参数复杂性。最后借助ECA注意力机制设计出MPCE模块，削弱复杂背景信息对钢表面缺陷检测的干扰,提升检测效率。最后，广泛的实验结果表明，ECC-YOLO在NEU-DET数据集上，该模型的mAP达到77.2%，相较于YOLOv7，ECC-YOLO的检测精度提高了10.1%，模型参数量减9.3%，该模型在钢表面缺陷检测中具有较好的综合性能。 |
| 英文摘要: |
| Aiming at the current problems of low efficiency and poor detection accuracy of steel surface defects, a model, named ECC-YOLO, is proposed for steel surface defects detection based on YOLOv7. Firstly, in order to improve the capability of feature map information characterization of the backbone network, a feature enhancement module ConvNeXt is introduced, which enhances the feature extraction capability of the model for fine cracks by fusing the depth separable convolution and the large kernel convolution, secondly, a C2fFB module is designed, which enhances the capability of extracting the feature information of the target and at the same time, reduces the computational volume and parameter complexity of the model significantly. Finally, the MPCE module is designed with the help of the ECA attention mechanism to weaken the interference of the complex background information on the steel surface defect detection and improve the detection efficiency. Finally, extensive experimental results show that the mAP of the model of ECC-YOLO reaches 77.2% on the NEU-DET dataset, and compared with YOLOv7, the detection accuracy of ECC-YOLO is improved by 10.1%, and the number of model parameters is reduced by 9.3%, which gives the model a better comprehensive performance in steel surface defect detection. |
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