![]() To test the performance of the proposed methods, a series of comparisons are conducted based on four different conditions: brightness, angle, distance, and cleanness. Illumination is considered in some of the proposed methods to see how it affects the recognition results. The recognition methods fall into two categories: one uses artificial intelligence (neural networks and fuzzy logic) as the backbone for recognition, while the other uses statistical approaches to segment the rust images. In this paper, several recognition methods are proposed and evaluated based on their recognition performance. However, to ensure the level of accuracy, appropriate recognition methods should be selected with care. The rust percentage is a crucial indicator in bridge painting warranty contracts which decides whether the painting contractor should re-do the painting work at the end of the warranty period. ![]() Through the use of digital image recognition methods, the rust percentage in a bridge painting image can be accurately computed, which is what an experienced bridge painting inspector cannot achieve. EVALUATION OF VARIOUS IMAGE RECOGNITION METHODS FOR BRIDGE PAINTING RUST INSPECTIONÄigital image recognition methods have been utilized for bridge painting rust inspection in the recent years.
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