| 单明陶,高玮玮.改进 YOLOv4 的内丝接头密封面缺陷检测算法[J].电子测量与仪器学报,2022,36(5):120-127 |
| 改进 YOLOv4 的内丝接头密封面缺陷检测算法 |
| Improved YOLOv4’s algorithm for detecting defects onthe sealing surface of inner wire joints |
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| DOI： |
| 中文关键词: 目标检测 YOLOv4 SENet SPP K-means++ |
| 英文关键词: |
| 基金项目:国家自然科学基金（61703268）项目资助 |
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| 摘要点击次数: 242 |
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| 中文摘要: |
| 针对传统目标检测算法对内丝接头密封面的缺陷识别率不高的问题,提出利用改进的 YOLOv4 算法对其进行检测。 首
先使用 K-means++聚类算法对目标样本进行先验框参数优化,提高先验框与特征图的匹配度;其次在主干网络嵌入 SENet 注意
力机制模块,强化图像关键信息,抑制图像背景信息,提高不易识别缺陷的置信度;然后在网络颈部增加 SPP 模块,增强主干网
络输出特征的接受域,分离出重要的上下文信息;最后采用收集的内丝接头密封面缺陷数据集分别对改进前后的 YOLOv4 进行
训练,并分别测试模型效果。 实验结果表明,YOLOv4 检测内丝接头密封面缺陷的性能较好,但有部分小目标漏检;改进后的模
型对小目标缺陷的检测表现优异,均值平均精度(mAP)达到了 87. 47%,相比于原始 YOLOv4 提升了 10. 2%,平均检测时间为
0. 132 s,实现了对内丝接头密封面缺陷的快速准确检测。 |
| 英文摘要: |
| Aiming at the problem of low recognition rate of traditional target detection algorithm for inner wire joint sealing surface
defects, an improved YOLOv4 algorithm was proposed to detect the defects. Firstly, k-means++ clustering algorithm is used to optimize
the parameters of the anchor frame of the target sample, and improve the matching degree between the anchor frame and the feature map;
Secondly, the SENet attention mechanism module is introduced into the backbone network to strengthen the key information of the image,
suppress the background information of the image, and improve the confidence of the defect that is not easy to identify; after that, the
SPP module is added to the neck of the network to enhance the acceptance domain of the backbone network output features and separate
the important context information; Finally, using the collected data set of inner wire joint sealing surface defects to train the original
YOLOv4 and the improved YOLOv4, and the performance of models were tested respectively on test set. The experimental results show
that the performance of YOLOv4 is good, but some small targets are missed; The improved model has excellent detection performance for
small target defects, the mean average accuracy (mAP) reaches 87. 47%, which is 10. 2% higher than the original YOLOv4, and the
average detection time is 0. 132 s, which realize the rapid and accurate detection of inner wire joint sealing surface defects. |
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