The System Research and Implementation for Autorecognition of the Ship Draft via the UAV

Abstract

The reading of the ship draft is an important step in the process of weighing and pricing. The traditional detection method is time-consuming and labor-consuming, and it is easy to lead to misdetection. In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. The experimental results show that the ship draft reading method proposed in this paper has obvious effects. The method has a good detection effect on high-quality images, and the accuracy rate can reach 98%. The accuracy rate can also reach 73% for the images with poor quality caused by improper capture, character corrosion, bad weather, etc. This method is a kind of artificial intelligence method with safe measurement process, high measurement effect, and accuracy, providing a new idea for related research.