Vision-based detection of container lock holes using a modified local sliding window method

EURASIP Journal on Image and Video Processing

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[1] 0000 0004 1791 7667, grid.263901.f, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China;Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu, China;0000 0004 1937 0482, grid.10784.3a, TStone Robotics Institute, The Chinese University of Hong Kong, Hong Kong SAR, China;

关键词: Container port;    Computer vision;    Container lock holes location;    Automatic handling;   

DOI  :  10.1186/s13640-019-0472-1

来源: publisher

【 摘 要 】

Container yards have been facing the increase of freight volume. In order to improve the efficiency of container handling, automatic stations have been established in many terminals. However, current container handling still needs a manual operation to locate container lock holes. Hence, it is inefficient and potential to risk workers’ health under long working hours. This paper presented a hybrid machine vision method to automatically recognize and locate container lock holes. The proposed method extracted the top area of the container from the multiple container areas, and then presented a new modified local sliding window to detect the keyhole region. The algorithm learned the histograms of oriented gradients (HOG) features using a multi-class support vector machine (SVM). Finally, the holes were located using direct least square fitting of ellipses. We carried an experiment under various weather and light conditions including nights and rainy days. The results showed that both the recognition and location accuracy outperformed the state-of-the-art results.

【 授权许可】

CC BY