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This paper analyzes the problems of existing container positioning methods and proposed a vision-based container position measuring system to provide precise parameters for container lifting operations. This system uses camera to get container information, then detects container corners by the method that combined with convolutional neural network and traditional image processing algorithm. This system is used to provide specific parameters associated with container lifting operation. In the first of detection, it uses the modified SSD (Single Shot MultiBox Detector) neural network to detect the coarse position of container corners in the image, second stage detection uses the usage of rectangle fitting to detect the precise position of corner holes in the coarse position. In the last step the offset distance and deflection angle were calculated by precise corner position. The experiment shows the detection rate of the proposed system reach 94%. The positioning errors between 14.3 and 19.6 mm for a frame rate of 10 fps are obtained.

An automated container terminal (ACT) is a cutting-edge type of container terminal that uses automated equipment and sensors to achieve autonomous applications such as container loading/unloading, horizontal transportation, and yard operations. It has integrated state-of-the-art sensing technologies, ensuring low operating costs, high throughput capacity, and enhanced management security. Vision-based measurement (VBM) is an especially advanced technology that obtains richer surrounding information from captured image data in comparison with other technologies. Due to its great potential capabilities, it has played a critically important role in ACT to realize productive vision-based tasks, e.g., target recognition, positioning, and geometric determination. This paper generally presents an overview of a VBM system, typical applications of VBM systems in ACT, as well as the challenges and future development trends of VBM in ACT.

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

Desirable patterns formed by microphase separation in block copolymers make these materials attractive for miniaturization of functional devices in which formation of nanostructures is highly deman...