Research on image detection algorithm of freight train brake shoe bolt and brake shoe fault

doi:10.11996/JG.j.2095-302X.2023010088

Abstract

Abstract:

The state of brake shoe bolt and brake shoe is of great importance to the safe operation of freight trains. Therefore, an improved SSD (single shot multi-box detector) target detection algorithm was proposed to detect the missing brake shoe bolt and brake shoe of freight trains. Firstly, the depthwise separable convolution module was introduced into the ResNet50 network model to reduce the number of parameters by about 50%, thereby improving the detection efficiency. Secondly, the improved ResNet50 network model was employed to replace the VGG16 network in SSD to improve the feature extraction capability of the SSD network model. Then, Conv5_3 and Conv7_2 were combined with Conv4_6 and Conv6_2 respectively by the method of high level feature and low level feature fusion to improve the detection accuracy. Finally, the network was trained to obtain more accurate weights through the self-built dataset of freight train brake component loss. The experimental results show that the improved SSD algorithm could attain an accuracy of 96.85% and a recall of 89.50% in brake shoe brazing loss detection, and an accuracy of 97.01% and a recall of 97.01% in the detection of brake shoe loss, thus meeting the requirement of missing brake shoe bolt and brake shoe detection.

Key words: target detection, single shot multi-box detector, depthwise separable convolution, residual structure

CLC Number:

TP391

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.

Figures/Tables 12

References 18

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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	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} 64 \\ 64 \\ \ 512 \\ \end{matrix} \right)\times 3,\ {{\text{s}}_{2}}$
Conv 3	38×38	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} \ 128 \\ \ 128 \\ \ 512 \\ \end{matrix} \right)\times 4,\ {{\text{s}}_{2}}$
Conv 4	38×38	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} 256 \\ 256 \\ \ 1024 \\ \end{matrix} \right)\times 6,\ {{\text{s}}_{1}}$
Conv 5	19×19	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} \begin{matrix} 256 \\ \ 256, \\ 512 \\ \end{matrix} & ds \\ \end{matrix} \right)\times 3,\ {{\text{s}}_{\text{2}}}$

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	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} 64 \\ 64 \\ \ 512 \\ \end{matrix} \right)\times 3,\ {{\text{s}}_{2}}$
Conv 3	38×38	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} \ 128 \\ \ 128 \\ \ 512 \\ \end{matrix} \right)\times 4,\ {{\text{s}}_{2}}$
Conv 4	38×38	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} 256 \\ 256 \\ \ 1024 \\ \end{matrix} \right)\times 6,\ {{\text{s}}_{1}}$
Conv 5	19×19	$\left( \begin{matrix} 1\times 1, \\ 3\times 3, \\ 1\times 1, \\ \end{matrix} \right.\left. \begin{matrix} \begin{matrix} 256 \\ \ 256, \\ 512 \\ \end{matrix} & ds \\ \end{matrix} \right)\times 3,\ {{\text{s}}_{\text{2}}}$

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

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(%)
特征提取网络	闸瓦钎丢失	闸瓦丢失
MobileNet	88.00	94.25
ResNet50	88.17	96.18
ResNet50dw	88.33	96.91