Robot vision
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
An Adaptive Texture and Shape Based Defect Classification
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Surface Tortuosity and its Application to Analyzing Cracks in Concrete
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
An Intelligent Real-time Vision System for Surface Defect Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A new approach to estimate fractal dimensions of corrosion images
Pattern Recognition Letters
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
NN automated defect detection based on optimized thresholding
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Crack detection in supported beams based on neural network and support vector machine
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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Vessel maintenance entails periodic visual inspections of internal and external parts of the vessel hull in order to detect cracks and corroded areas. Typically, this is done by trained surveyors at great cost. Clearly, assisting them during the inspection process by means of a fleet of robots capable of defect detection would decrease the inspection cost. In this paper, two algorithms are presented for visual detection of the aforementioned two kinds of defects. On the one hand, the crack detector is based on a percolation process that exploits the morphological properties of cracks in steel surfaces. On the other hand, the corrosion detector follows a supervised classification approach taking profit from the spatial distribution of color in rusty areas. Both algorithms have shown successful rates of detection with close to real-time performance.