计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230800018-5.doi: 10.11896/jsjkx.230800018
黄海新, 吴迪
HUANG Haixin, WU Di
摘要： 钢材表面缺陷检测在实际生产中非常重要。为了准确检测缺陷,设计了一种基于改进的YOLOv7的钢材表面缺陷检测模型。首先在骨干网络结构中引入Ghost模块,增强模型提取特征和识别小特征的能力,同时降低模型参数量;其次在池化模块中嵌入注意力机制;最后通过引入EIOU改善损失函数,从而更好地优化 YOLOv7 网络模型,且可以更好地处理样本的不平衡,从而达到更好的优化相似度。实验结果表明,与原模型相比,所提模型mAP达到76.9%,提高了4.2%。该模型可以满足钢表面缺陷的准确检测和识别需求。
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