| 陈亮,杨贤昭,刘惠康.基于改进Deeplabv3+网络的线缆表面缺陷检测研究[J].高技术通讯(中文),2021,31(9):986~992 |
| 基于改进Deeplabv3+网络的线缆表面缺陷检测研究 |
| Research on cable surface defect detection based on improved Deeplabv3+ network |
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| DOI：10.3772/j.issn.1002-0470.2021.09.010 |
| 中文关键词: 线缆表面缺陷； Deeplabv3+； 空洞卷积； 并联结构； 准确度 |
| 英文关键词: surface defects of cable, Deeplabv3+, dilated convolution, parallel structure, accuracy |
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| 中文摘要: |
| 为了提高线缆表面缺陷检测正确率，本文提出一种改进Deeplabv3+网络的图像分割方法并将其应用于线缆表面缺陷检测。该方法基于Deeplabv3+网络骨架不变，将空间金字塔结构由4个空洞卷积改为8个空洞卷积并在其后增加1×1的卷积环节；同时在解码融合后用一个并联结构来减少整个网络传输过程的信息丢失。利用改进的算法对线缆表面缺陷图片数据集训练和测试，结果表明改进算法在准确度和平均交并比(MIOU)较原始的Deeplabv3+分析效果更好；相较于边缘分割和阈值分割等算法，改进算法提高了线缆表面缺陷检测的准确率。 |
| 英文摘要: |
| In order to improve the accuracy of cable surface defect detection, an improved image segmentation method of Deeplabv3+ network is proposed and applied to cable surface defect detection. Based on Deeplabv3+ network skeleton unchanged, the spatial pyramid structure is changed from 4 dilated convolutions to 8 dilated convolutions and then 1×1 convolution is added. At the same time, a parallel structure is used to reduce the information loss during the whole network transmission process after decoding and fusion. The improved algorithm is used to train and test the cable surface defect image data set, and the results show that it is better than the original Deeplabv3+ analysis in accuracy and mean intersection over union (MIOU). Compared with edge segmentation, threshold segmentation and other algorithms, the proposed algorithm improves the detection accuracy of cable surface defects. |
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