A fast stitching method for container images using texture and weighted speed

Abstract

With the rapid development of oceans economy, a huge number of shipping containers are transported all around the world. To reduce the risk of container damages during the transportation, existing solution relies mainly on human beings to observe the container appearance before or after it enters a dock, which is time-consuming and inaccurate. To solve this problem, one intelligent approach is to develop an automatic container damage detection framework based on computer vision techniques. But how to obtain the panorama images for container damage detection is a challenging issue. In this paper, a real-time container panorama producing system is developed based on container surveillance videos, which is implemented by container image stitching with texture features. When there is no reliable offset, the weighted speed for splicing is used. Experimental results indicate that the developed system could achieve approving results in a real-time manner.

Multitype Damage Detection of Container Using CNN Based on Transfer Learning

“…ey believe that incorrect loading and lack of loading safety measures in the trailer may cause damage to the container and cargo and may cause further damage to the container and load in the subsequent process. Meng et al [28] developed a real-time container panoramic generation system based on container surveillance video, aiming to solve the problem of obtaining panoramic images of container damage detection. e real-time container panorama generation system uses texture feature stitching technology, which will support container damage detection based on port monitoring equipment.…”