Automated visual inspection of target parts for train safety based on deep learning

作者：Zhou, Fuqiang^*; Song, Ya; Liu, Liu; Zheng, Dongtian

来源：IET Intelligent Transport Systems, 2018, 12(6): 550-555.

DOI：10.1049/iet-its.2016.0338

• learning (artificial intelligence)
• feature extraction
• data mining
• railway safety
• traffic engineering computing
• inspection
• neural nets
• automated visual inspection
• target parts
• train safety
• deep learning
• inspection accuracy
• image recognition
• autonomous information mining
• stacked auto-encoder convolutional neural network
• composite neural network
• SAE-CNN
• training efficiency
• centre plate bolts
• moving freight car

Visual inspection of target parts is a common approach to ensuring train safety. However, some key parts, such as fastening bolts, do not possess sufficient feature information, because they are usually small, polluted, or obscured. These factors affect inspection accuracy and can lead to serious accidents. Therefore, traditional visual inspection relying on feature extraction cannot always meet the requirements of high-accuracy inspection. Deep learning has considerable advantages in image recognition for autonomous information mining, but it requires a considerable amount of computation. To resolve the issues mentioned above, this study proposes a method that combines traditional visual inspection with deep learning. Traditional feature extraction is used to locate the targets approximately, which makes the deep learning purposeful and efficient. A composite neural network, stacked auto-encoder convolutional neural network (SAE-CNN), is provided to further improve the training efficiency. A SAE is added to a CNN so that the network can obtain optimum results faster and more accurately. Taking the inspection of centre plate bolts in a moving freight car as an example, the overall system and specific processes are described. The study results showed satisfactory accuracy. A related analysis and comparative experiment were also conducted.

• 出版日期2018-8
• 单位北京航空航天大学