A Ship Draft Line Detection Method Based on Image Processing and Deep Learning
Zhong Wang, Peibei Shi, Chao Wu · Journal of Physics Conference Series · 2020
Abstract The traditional ship draft detection method mainly adopts the method of the human eye observation, which has the problems of large precision error and slow detection speed. Aiming at this problem, this paper proposes a ship draft line detection method based on image processing and deep learning. The color image is first transformed into a grayscale image, and the corner detection is carried out by Harris operator. Contour map is obtained using Canny operator for edge detection, and line segment detection is performed using Hough algorithm. The water surface is determined by calculating the lowest position of the corner point. Then the grayscale image is binarized to increase the contrast of the image, the digital area is determined by the corner positions of the highest and lowest points, and finally the convolutional neural network is used to perform character recognition and calculate the draft of the ship. Tested on videos of various scenes, the error of the proposed method is less than 1cm.