基于改进YOLOv7的钢材表面缺陷检测
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引用本文:付帅,凌铭,楚东港.基于改进YOLOv7的钢材表面缺陷检测[J].计算技术与自动化,2024,(2):10-16
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作者单位
付帅,凌铭,楚东港 (上海工程技术大学 电子与电气工程学院上海 201620) 
中文摘要:钢材表面缺陷对于钢材行业来说是一个巨大的挑战。针对传统的钢材缺陷检测方法存在着效率低、检测精度不高等问题,基于YOLOv7设计了一种AFSD-YOLOv7模型进行实时的钢材表面缺陷检测。首先,在YOLOv7模型中使用一种轻量化卷积结构替换标准卷积结构,,以加速模型的推理过程;然后采用快速空间金字塔池化结构替换原始空间金字塔池化结构,以加速网络的特征提取过程;最后添加改进的ECA-Net注意力机制,以提升模型检测精度。实验结果表明,AFSD-YOLOv7能够对钢材缺陷进行有效识别,相比YOLOv7模型,计算量减少了54.8%,mAP提高了3.2%,对于钢材表面缺陷检测具有实际应用价值。
中文关键词:钢材  缺陷检测  YOLOv7  神经网络  深度学习  注意力机制  标准卷积
 
Steel Surface Defect Detection Based on Improved YOLOv7
Abstract:Surface defects on steel are a significant challenge for the steel industry. Traditional steel defect detection methods suffer from low efficiency and accuracy. To address these issues, an AFSD-YOLOv7 model has been designed for real-time steel surface defect detection. First, a lightweight convolutional structure was used to replace the standard convolutional structure in the YOLOv7 model, speeding up the inference process. Next, a fast spatial pyramid pooling structure was used to replace the original spatial pyramid pooling structure to accelerate the network’s feature extraction process. Finally, an improved ECA-Net attention mechanism was added to enhance the model′s detection accuracy. Experimental results show that AFSD-YOLOv7 can effectively identify steel defects. Compared to the YOLOv7 model, AFSD-YOLOv7 reduces computation by 54.8% and improves mAP by 3.2%, indicating significant practical value for steel surface defect detection.
keywords:steel  defect detection  YOLOv7  neural networks  deep learning  attention mechanism  standard convolution
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