Abstract
Serious noise pollution and background interference bring great difficulties to infrared image segmentation of electronic equipment. A novel infrared image segmentation method based on multi-information fused fuzzy clustering method is proposed in this article. Firstly, saliency detection is performed on the infrared image to obtain the saliency map, which determines the initial clustering center and enhances the contrast of the original infrared image. Secondly, the weighting exponent in the objective function is adjusted adaptively. Then local and global spatial constraints are added to the objective function of the fuzzy clustering method, which can reduce the noise and background interference. Finally, the Markov constrained field is calculated according to the initial segmentation result. After that the joint field of fuzzy clustering field and the Markov random field is constructed to obtain the optimized segmentation result. The algorithm is evaluated on the infrared images of electrical equipment, and the experimental results show that the proposed method is robust to noise and complicated background. Compared with other methods, the proposed method improves the average segmentation accuracy and T measure by about 10% and 13%.
Highlights
In recent years, substation inspection is gradually becoming unmanned, and robots have gradually been applied to substation inspections.[1]
The HHU-IR150 data set includes 150 electrical equipment infrared images, all of which were taken at the real scene of substations
Algorithm 1: Infrared image segmentation based on multiinformation fused fuzzy C-means (FCM)
Summary
Substation inspection is gradually becoming unmanned, and robots have gradually been applied to substation inspections.[1]. Information processing on the robot platform is becoming the key to electrical equipment intelligent inspection. Faults of electrical equipment inspection are one of the important problems that can be solved by information processing of robot platform. According to the above steps, the saliency map is obtained. The initial clustering center has a great influence on the convergence speed and segmentation accuracy of the algorithm. The initial clustering centers can be obtained according to the statistical characteristics of the saliency map
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