Recognizing Trained and Untrained Obstacles around a Port Transfer Crane Using an Image Segmentation Model and Coordinate Mapping between the Ground and Image

Abstract

Container yard congestion can become a bottleneck in port logistics and result in accidents. Therefore, transfer cranes, which were previously operated manually, are being automated to increase their work efficiency. Moreover, LiDAR is used for recognizing obstacles. However, LiDAR cannot distinguish obstacle types; thus, cranes must move slowly in the risk area, regardless of the obstacle, which reduces their work efficiency. In this study, a novel method for recognizing the position and class of trained and untrained obstacles around a crane using cameras installed on the crane was proposed. First, a semantic segmentation model, which was trained on images of obstacles and the ground, recognizes the obstacles in the camera images. Then, an image filter extracts the obstacle boundaries from the segmented image. Finally, the coordinate mapping table converts the obstacle boundaries in the image coordinate system to the real-world coordinate system. Estimating the distance of a truck with our method resulted in 32 cm error at a distance of 5 m and in 125 cm error at a distance of 30 m. The error of the proposed method is large compared with that of LiDAR; however, it is acceptable because vehicles in ports move at low speeds, and the error decreases as obstacles move closer.

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

“…For the extraction of edge information, Gu [29] used the improved wavelet mode maximum algorithm to extract image edges, which can obtain edge image information with better clarity and connectivity. Yu [30] extracted the boundary of an obstacle from the semantic segmentation result by applying pixel filtering. For irregular obstacles, Bai [31] conducted grid preprocessing and convex preprocessing for concave obstacles, which enhanced the safety of UAV path obstacle avoidance.…”