Underwater cable detection in the images using edge classification based on texture information

In this paper, a new approach is proposed for detection of an underwater cable, which makes an Autonomous Underwater Vehicle (AUV) capable for automatic tracking. In this approach instead of traditional image segmentation, first, edges of the images are extracted. Then they are classified using Multilayer Perceptron (MLP) neural network and Support Vector Machine (SVM) using texture information. Then the edge points belonged to the background information are removed and the remaining ones are used for the next processes. Finally, the filtered edges are repaired by morphological operators and are fed into the Hough transform for cable detection. Some texture information methods are used for feature extraction but the results confirm that the 2D Fourier transform in combination with MLP network is the best method for edge classification in this environment. Hough transform, is used in two strategies, which in the first one, the whole information of the edges in the image, are used for line detection, and in the second approach because of curve like shape of the cable, a center part of the image, is used for line detection. In the experiments, many different scenes was used for testing the cable detection algorithm, which first method, resulted to good accuracy but the second one, provided better recognition rate for the cable detection task.