Journal of Graphics ›› 2023, Vol. 44 ›› Issue (1): 88-94.DOI: 10.11996/JG.j.2095-302X.2023010088
• Image Processing and Computer Vision • Previous Articles Next Articles
Received:
2022-04-27
Revised:
2022-07-05
Online:
2023-10-31
Published:
2023-02-16
Contact:
ZHAO Jun
About author:
GU Yu (1996-), master student. His main research interests cover computer vision and deep learning. E-mail:2378209338@qq.com
Supported by:
CLC Number:
GU Yu, ZHAO Jun. Research on image detection algorithm of freight train brake shoe bolt and brake shoe fault[J]. Journal of Graphics, 2023, 44(1): 88-94.
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URL: http://www.txxb.com.cn/EN/10.11996/JG.j.2095-302X.2023010088
层数 | 输出尺寸 | 结构、类型 |
---|---|---|
Conv 1 | 150×150 | 7×7, 64, s2 |
Pool 1 | 75×75 | 3×3, max, s2 |
Conv 2 | 75×75 | |
Conv 3 | 38×38 | |
Conv 4 | 38×38 | |
Conv 5 | 19×19 |
Table 1 Diagram of residual block
层数 | 输出尺寸 | 结构、类型 |
---|---|---|
Conv 1 | 150×150 | 7×7, 64, s2 |
Pool 1 | 75×75 | 3×3, max, s2 |
Conv 2 | 75×75 | |
Conv 3 | 38×38 | |
Conv 4 | 38×38 | |
Conv 5 | 19×19 |
数据集 | 闸瓦钎 丢失 | 闸瓦钎 正常 | 闸瓦 丢失 | 闸瓦钎 正常 |
---|---|---|---|---|
训练集 | 981 | 1359 | 479 | 1917 |
测试集 | 109 | 155 | 51 | 213 |
总计 | 1090 | 1514 | 530 | 2130 |
Table 2 Category and quantity of experimental data sets
数据集 | 闸瓦钎 丢失 | 闸瓦钎 正常 | 闸瓦 丢失 | 闸瓦钎 正常 |
---|---|---|---|---|
训练集 | 981 | 1359 | 479 | 1917 |
测试集 | 109 | 155 | 51 | 213 |
总计 | 1090 | 1514 | 530 | 2130 |
特征提取网络 | 分类准确度(%) | |
---|---|---|
闸瓦钎丢失 | 闸瓦丢失 | |
MobileNet | 88.00 | 94.25 |
ResNet50 | 88.17 | 96.18 |
ResNet50dw | 88.33 | 96.91 |
Table 3 Comparative experimental results of backbone
特征提取网络 | 分类准确度(%) | |
---|---|---|
闸瓦钎丢失 | 闸瓦丢失 | |
MobileNet | 88.00 | 94.25 |
ResNet50 | 88.17 | 96.18 |
ResNet50dw | 88.33 | 96.91 |
算法模型 | 闸瓦丢失(%) | 闸瓦钎丢失(%) | 单幅图像 平均检测用时(ms) | ||
---|---|---|---|---|---|
精确度 | 召回率 | 精确度 | 召回率 | ||
Faster-RCNN | 91.49 | 98.47 | 80.95 | 85.04 | 85.26 |
YOLOv3 | 93.64 | 78.63 | 96.66 | 85.76 | 27.09 |
VGG16-SSD | 86.79 | 95.83 | 91.67 | 84.62 | 16.67 |
ResNet50-SSD | 94.19 | 95.29 | 94.40 | 87.62 | 20.08 |
ResNet50-SSD+feature fusion | 96.57 | 97.76 | 96.48 | 90.09 | 28.06 |
改进后SSD | 97.01 | 97.01 | 96.85 | 89.50 | 19.13 |
Table 4 Comparison of six detection network
算法模型 | 闸瓦丢失(%) | 闸瓦钎丢失(%) | 单幅图像 平均检测用时(ms) | ||
---|---|---|---|---|---|
精确度 | 召回率 | 精确度 | 召回率 | ||
Faster-RCNN | 91.49 | 98.47 | 80.95 | 85.04 | 85.26 |
YOLOv3 | 93.64 | 78.63 | 96.66 | 85.76 | 27.09 |
VGG16-SSD | 86.79 | 95.83 | 91.67 | 84.62 | 16.67 |
ResNet50-SSD | 94.19 | 95.29 | 94.40 | 87.62 | 20.08 |
ResNet50-SSD+feature fusion | 96.57 | 97.76 | 96.48 | 90.09 | 28.06 |
改进后SSD | 97.01 | 97.01 | 96.85 | 89.50 | 19.13 |
Fig. 8 Comparison of detection effect of original SSD and improved SSD ((a) The original SSD algorithm for brake shoe bolt fault detection; (b) Algorithm of this paper for accurate detection)
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