Artificial intelligence application to bridge painting assessment

Digital image recognition has been experimented for steel bridge painting assessment by the Indiana Department of Transportation (INDOT) in September 1999. Although the application was successfully carried out as a whole, there are still some minor problems left to be improved. Nonuniform illumination is one of the problems that affect the accuracy of recognition results. To address this problem, the neuro-fuzzy recognition approach (NFRA) is proposed, which segments an image into three areas based on illumination and conducts area-based thresholding with the help of an artificial neural network (ANN) and a fuzzy adjustment system. In this paper, the framework of NFRA will be presented, followed by the application of NFRA to steel bridge painting assessment and its performance comparison with the multiresolution pattern classification (MPC) method and the iterated conditional modes (ICM) algorithm. The conclusions will be presented last.