Due to the excessively dense steel coil layers on the end face of the rolled coil, it is difficult to extract the defect area of the end face of rolled steel by the image segmentation methods such as edge detection and threshold segmentation. Aiming at the dense texture features of the end face of rolled steel coil, a method of detecting the defects of the end face of steel coils was proposed. Based on the theoretical technology of machine vision, this paper proposed a double threshold method to extract potential defect area and eliminate the background area and the completely defect-free area. In the double threshold method, we utilize the Canny operator and the PPHT (Progressive Probabilistic Hough Transform) to adjust the direction of the image block on the end face of the steel coils, makes the texture direction on each image block consistent. Then, Gaussian steerable filter, followed by second Canny and PPHT, was applied to enhance image. After the double threshold method, projective integral of digital image was utilized to extract the feature of the potential defects area. Finally, the SVM (Support Vector Machine) is applied to determine the type of the defect. The results show that the method of detecting the defects of the end face of steel coils can accurately and quickly detect defects even on the end face image of the steel coils with dense layers.
With the development of marine resources, the USV (Unmanned Surface Vehicle) was widely used as a platform for autonomous navigation in the marine environment. In order to ensure the safe navigation of USV, this paper proposed a sea surface obstacle detection method based on probability graphical model and sea-sky-line. Our method utilized the SLIC algorithm to segment the sea surface image for image pre-processing. Then, we proposed the superpixel-based probability graphical model to segment the image, and the sea surface image would be divided into three main semantic regions and an obstacle region. Finally, we proposed a sea-sky-line detection algorithm. Based on this, obstacles within the sea-sky-line would be detected. The accuracy of this method has reached 82.1%, and the recall rate has reached 92.0%. The method can effectively avoid the interference of sea surface reflection and objects such as clouds in the sky, and has a good effect on the detection of obstacles.