{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T23:55:24Z","timestamp":1772236524529,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2011,7,25]],"date-time":"2011-07-25T00:00:00Z","timestamp":1311552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km\/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km\/h.<\/jats:p>","DOI":"10.3390\/s110807364","type":"journal-article","created":{"date-parts":[[2011,7,25]],"date-time":"2011-07-25T12:35:29Z","timestamp":1311597329000},"page":"7364-7381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways"],"prefix":"10.3390","volume":"11","author":[{"given":"Jinfeng","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Wei","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Manhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yongjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Haibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]}],"member":"1968","published-online":{"date-parts":[[2011,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"De Ruvo, P, Distante, A, Stella, E, and Marino, F (2009, January 7\u201310). A GPU-based Vision System for Real Time Detection of Fastening Elements in Railway Inspection. Cairo, Egypt.","DOI":"10.1109\/ICIP.2009.5414438"},{"key":"ref_2","unstructured":"Deutschl, E, Gasser, C, Niel, A, and Werschonig, J (2004, January 14\u201317). Defect Detection on Rail Surfaces by a Vision Based System. Parma, Italy."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yella, S, Ghiamati, S, and Dougherty, M (2009, January 11\u201314). Condition Monitoring of Wooden Railway Sleepers Using Time-Frequency Techniques and Pattern Classification. San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5346713"},{"key":"ref_4","unstructured":"Sawadisavi, SV, Edwards, JR, Resendiz, E, Hart, JM, Barkan, CPL, and Ahuja, N (2009, January 11\u201315). Machine-Vision Inspection of Railroad Track. Washington, DC, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1299\/jmtl.3.154","article-title":"Condition monitoring of railway track using in-service vehicle","volume":"1","author":"Mori","year":"2010","journal-title":"J. Mech. Syst. Transp. Logist"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lin;, J, Luo, S, Li, Q, Zhang, H, and Ren, S (2009, January 5\u20138). Real-time rail head surface defect detection: A geometrical approach. Seoul Olympic Parktel, Seoul, Korea.","DOI":"10.1109\/ISIE.2009.5214088"},{"key":"ref_7","unstructured":"Al-Nuaimy, W, Eriksen, A, and Gasgoyne, J (2004, January 21\u201324). Train-Mounted GPR for High-Speed Rail Trackbed Inspection. Ground Penetrating Radar. Delft, The Netherlands."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1109\/TIA.2003.816474","article-title":"Rail defect diagnosis using wavelet packet decomposition","volume":"39","author":"Toliyat","year":"2003","journal-title":"IEEE Trans. Ind. Appl"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hsieh, H-Y, Chen, NM, and Liao, CL (2007, January 13\u201316). Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems. Pueblo, CO, USA.","DOI":"10.1115\/JRC\/ICE2007-40080"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1109\/TSMCC.2007.893278","article-title":"A real-time visual inspection system for railway maintenance: Automatic hexagonal-headed bolts detection","volume":"37","author":"Marino","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. C"},{"key":"ref_11","unstructured":"NStella, E, Mazzeo, P, Nitti, M, Cicirelli, G, Distante, A, and D\u2019Orazio, T (2002, January 3\u20136). Visual Recognition of Missing Fastening Elements for Railroad Maintenance. Singapore."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3390\/s110100090","article-title":"3D geometrical inspection of complex geometry parts using a novel laser triangulation sensor and a robot","volume":"11","author":"Brosed","year":"2011","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.3390\/s100201041","article-title":"A multiscale region-based motion detection and background subtraction algorithm","volume":"10","author":"Varcheie","year":"2010","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TPAMI.2009.199","article-title":"Rigid shape matching by segmentation averaging","volume":"32","author":"Wang","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TPAMI.2005.220","article-title":"Efficient shape matching using shape contexts","volume":"27","author":"Mori","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_16","unstructured":"Rao, AR, and Schunck, BG (1989, January 4\u20138). Computing Oriented Texture Fields. San Diego, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Soifer, VA, Kotlyar, VV, Khonina, SN, and Skidanov, RV (1996, January 25\u201329). Fingerprint Identification Using the Directions Field. Vienna, Austria.","DOI":"10.1109\/ICPR.1996.547014"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1109\/34.9107","article-title":"Hierarchical chamfer matching: A parametric edge matching algorithm","volume":"10","author":"Borgefors","year":"1988","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/34.574805","article-title":"Linear discriminant analysis for two classes via removal of classification structure","volume":"19","author":"Aladjem","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/TNN.2006.885038","article-title":"Weighted piecewise LDA for solving the small sample size problem in face verification","volume":"18","author":"Kyperountas","year":"2007","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8073","DOI":"10.3390\/s91008073","article-title":"Identification of tea storage times by linear discrimination analysis and back-propagation neural network techniques based on the eigenvalues of principal components analysis of E-nose sensor signals","volume":"9","author":"Yu","year":"2009","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3390\/s8020817","article-title":"Parallel Algorithm for GPU processing; for use in high speed machine vision sensing of cotton lint trash","volume":"8","author":"Pelletier","year":"2008","journal-title":"Sensors"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/8\/7364\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:56:52Z","timestamp":1760219812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/8\/7364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,7,25]]},"references-count":23,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2011,8]]}},"alternative-id":["s110807364"],"URL":"https:\/\/doi.org\/10.3390\/s110807364","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,7,25]]}}}