{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:05Z","timestamp":1777705205079,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,3,2]]},"abstract":"<jats:p>The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.<\/jats:p>","DOI":"10.3233\/jifs-192141","type":"journal-article","created":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T13:57:00Z","timestamp":1610114220000},"page":"3931-3943","source":"Crossref","is-referenced-by-count":5,"title":["Intrusion detection of railway clearance from infrared images using generative adversarial networks"],"prefix":"10.1177","volume":"40","author":[{"given":"Yundong","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"},{"name":"Key Laboratory of Large Structure Health Monitoring and Control, Shijiazhuang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-192141_ref1","first-page":"1","article-title":"Curvelet transform-based identification of void diseases in ballastless track by ground-penetrating radar","volume":"29","author":"Yang","year":"2019","journal-title":"Struct Control Health Monit"},{"key":"10.3233\/JIFS-192141_ref2","unstructured":"Nefti S. and Oussalah M. , A neural network approach for railway safety prediction, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. 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