{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:45:26Z","timestamp":1776811526063,"version":"3.51.2"},"reference-count":11,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,9,5]]},"abstract":"<jats:p>The automation transformation of container lifting operations is one of the main technical issues in Rail-Truck intermodal transportation. To solve this problem, this paper analyzes the advantages and disadvantages of several existing container positioning methods, and proposed a container corner holes location detection method based on lightweight convolutional neural network and adaptive morphological image processing algorithm. This method locates the container through a visual sensor that shoots from top to bottom. In order to improve the positioning accuracy and calculation speed while maintaining a high recognition rate, this method first uses a lightweight SSD detector to quickly detect the rough coordinates of the container corner hole in the image. On this basis, the smallest rectangle detection method based on adaptive HSV filtering is used to detect the precise coordinates of the corner hole in the image. Experiments show that the recognition rate of this algorithm in the initial positioning process reaches 94.2%, the recognition rate of secondary positioning reaches 87.4%, and the final positioning error is lower than 3.765 pixels and 2.81 pixels in the heading and lateral directions, respectively. The overall calculation time of the algorithm can realize the positioning calculation of 30 frames per second, which shows that this method takes into account the characteristics of high measurement accuracy and high calculation speed on the basis of high recognition rate.<\/jats:p>","DOI":"10.3233\/jcm-226135","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T12:14:10Z","timestamp":1653999250000},"page":"1559-1571","source":"Crossref","is-referenced-by-count":2,"title":["Two-stage container keyhole location algorithm based on optimized SSD and adaptive threshold"],"prefix":"10.66113","volume":"22","author":[{"given":"Qingfeng","family":"Huang","sequence":"first","affiliation":[{"name":"Logistic Engineering School, Shanghai Maritime University, Shanghai, China"},{"name":"China Communications Construction Company Limited, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Logistic Engineering School, Shanghai Maritime University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yage","family":"Huang","sequence":"additional","affiliation":[{"name":"Logistic Engineering School, Shanghai Maritime University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Mi","sequence":"additional","affiliation":[{"name":"Logistic Engineering School, Shanghai Maritime University, Shanghai, China"},{"name":"Container Supply Chain Technology Engineering Research Center, Ministry of Education, Shanghai Maritime University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai SMU Vision Smart Technology Ltd., Shanghai, 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under bad weather conditions by image restoration","volume":"61","author":"Kawai","year":"2012","journal-title":"IEEE Trans Instrum Meas."},{"issue":"6","key":"10.3233\/JCM-226135_ref13","first-page":"1229","article-title":"Review of convolutional neural network","volume":"40","author":"Zhou","year":"2017","journal-title":"Chin J Comput."},{"key":"10.3233\/JCM-226135_ref14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.compag.2016.11.021","article-title":"Automatic crop detection under field conditions using the HSV colour space and morphological operations","volume":"133","author":"Hamuda","year":"2017","journal-title":"Comput Electr Agric."},{"issue":"3","key":"10.3233\/JCM-226135_ref15","first-page":"184","article-title":"Life jacket detection algorithm based on HSV color feature and contour area","volume":"52","author":"Yang","year":"2016","journal-title":"Comput Eng Appl."},{"issue":"14","key":"10.3233\/JCM-226135_ref16","first-page":"11","article-title":"Image edge detection algorithm and its new development","volume":"54","author":"Zhang","year":"2018","journal-title":"Comput Eng Appl."},{"issue":"11","key":"10.3233\/JCM-226135_ref17","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans Pattern Anal Mach Intell."}],"container-title":["Journal of Computational Methods in Sciences and 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