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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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 |
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Steel Surface Defect Detection Based on Improved YOLOv7 |
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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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