Detection of Containerized Containers Based on Computer Vision

The detection of containerized containers is the core of intelligent de-palletizing system, which is lack of robustness due to factors such as the gaps between the containers, the patterns on the surfaces of containers, noises and ambient light. For this reason, a containers detection algorithm based on computer vision is proposed in this paper. This method used line merging operations to compensate for the broken lines in the Canny edge detection and LSD line extraction process. Secondly, based on the geometric characteristics of the containerized containers, four general rectangular structure elements are defined, which are used to construct the rectangles by a specific matching search algorithm. Then, based on the improved mean-shift clustering algorithm, cluster centers are used to replace a cluster of rectangles, which are used for x-axis and y-axis clustering operations, so as to eliminate the false detections caused by the factors such as patterns and straps. Finally, the non-maximum suppression algorithm is used to screen out the accurate rectangular contours. Experiments have verified that the detection method has an accuracy rate of about 99% for the identification of solid-colored containers. It also has good robustness for containers with patterns, straps, stains, and deformations. The recognition accuracy rate is about 95%.