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Received:April 22, 2024 Revised:May 29, 2024
Received:April 22, 2024 Revised:May 29, 2024
中文摘要: 钢材表面缺陷的检测质量直接影响工业生产安全性和机器性能质量, 现实工厂钢材质量把控受限于设备条件, 在实现高精度强实时的检测效果仍面临着挑战. 为了解决这一问题, 提出一种多尺度融合的轻量级YOLOv8n检测算法. 首先引入一种结合HGnetv2与RepConv的轻量级多尺度融合主干网络(RepHGnetv2), 提高Backbone的特征提取能力与泛化能力同时降低了模型的复杂度; 在Head部分, 利用ADown下采样模块替换原算法的普通卷积(Conv), 降低计算量并提高语义保留能力; 最后将原算法的Loss函数替换为SlideLoss, 改善样本之间不平衡的问题. 在NEU-DET数据集上进行消融与对比实验, 改进算法与原算法相比, mAP@0.5提升6.7%, Precision提升9.3%, 模型大小下降25.5%, 计算量下降了17.2%, FPS也有一定的提升; 并在VOC2012数据集上进行了通用性对比实验, 实验结果表明改进算法可以有效提高缺陷检测精度与效率, 同时具有较好的通用性.
中文关键词: 钢材表面缺陷检测 轻量级YOLOv8n RepHGnetv2 ADown SlideLoss
Abstract:The quality of steel surface defect inspection directly affects industrial production safety and machine performance. However, in real factories, steel quality control is limited by equipment conditions, making it challenging to achieve high-precision and real-time inspection. To solve this problem, a lightweight YOLOv8n detection algorithm with multi-scale fusion is proposed. Firstly, a lightweight multi-scale fusion backbone network (RepHGnetv2) is introduced, combining HGnetv2 and RepConv to improve the feature extraction and generalization capabilities of Backbone and reduce the complexity of the model. In the Head part, the ordinary convolution of the original algorithm is replaced with the ADown downsampling module, which reduces computational complexity and improves semantic retention. Finally, the loss function of the original algorithm is replaced by SlideLoss to address sample imbalance. Ablation and comparison experiments are conducted on the NEU-DET dataset. Compared with the original algorithm, the improved algorithm increases precision by 9.3%, reduces the model size by 25.5%, decreases computational complexity by 17.2%, and improves FPS to a certain extent. Comparative experiments are conducted on the VOC2012 dataset to evaluate the generalizability of the improved algorithm, and the results show that the improved algorithm exhibits strong generalizability and effectively improves the accuracy and efficiency of defect detection.
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基金项目:国家自然科学基金(62173171)
引用文本:
杨本臣,李世熙,金海波,康洁.多尺度融合的轻量级钢材表面缺陷检测.计算机系统应用,2024,33(11):58-67
YANG Ben-Chen,LI Shi-Xi,JIN Hai-Bo,KANG Jie.Lightweight Steel Surface Defect Detection with Multiscale Fusion.COMPUTER SYSTEMS APPLICATIONS,2024,33(11):58-67
杨本臣,李世熙,金海波,康洁.多尺度融合的轻量级钢材表面缺陷检测.计算机系统应用,2024,33(11):58-67
YANG Ben-Chen,LI Shi-Xi,JIN Hai-Bo,KANG Jie.Lightweight Steel Surface Defect Detection with Multiscale Fusion.COMPUTER SYSTEMS APPLICATIONS,2024,33(11):58-67