Abstract
With the development of container port automation, the automated vision systems for containers have been widely used in automated ports. This paper presents a rapid automated vision system for container corner casting recognition. The histograms of oriented gradients (HOG) descriptors are used to preprocess the image of the container and the vectors of HOG are then built. A trained support vector machine (SVM) classifier is applied to recognize the right corner casting of the container. At last, through symmetry, a flipping mirror algorithm is used for quick left corner casting recognition. The experimental results show this algorithm scans and detect the two corner castings of the container almost twice as fast as the traditional algorithms.
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