Fastener Defect Detection Algorithm Based on Cost-Sensitive Convolutional Neural Network

To solve the imbalance problem of fastener dataset, the cost-sensitive strategy was introduced to improve the convolutional neural network algorithm, and to detect fastener defects, such as fracture and loss. Based on the idea of AdaBoost, the algorithm assigned different weights to each sample in the overall error function during the training process, and constantly updated them according to the error rate of the previous models. The algorithm focused on the hard-to-learn samples in each category, and normalized the updated weights according to the category so as to increase the attention on the minor class samples. The effectiveness of the algorithm was verified by comparative experiments on two fastener data sets of the ballastless track of high-speed railway and the ballasted track of conventional speed railway. Meanwhile, G-mean was introduced as an evaluation index to balance the recall rates of different categories. Results show that: the improved algorithm is applied to the fastener data sets of the ballastless track of high-speed railway and ballast track of conventional speed railway, the G-mean values of the improved algorithm are increased by more than 10% and 25% respectively compared with the original algorithm, and 13% and 39% respectively higher than traditional fastener identification methods. © 2021, Editorial Department of China Railway Science. All right reserved.