Abstract
A Corrosion Management Expert System (CMES) was developed to improve inspection and damage assessment of existing steel transmission structures. CMES analyzes digital images of the corroded steel towers to classify corrosion type and severity. By employing an artificial neural network and RGB color model, CMES classifies pixels into a set of predefined colors. Since RGB is an additive color model, it is sensitive to environmental effects such as sunlight, shadows, etc., which will alter the red, green, and blue pixel values and may adversely affect the CMES's ability to accurately recognize the true pixel color and the image. For example, if a shadow covers part of the image, the system will identify the dark, shadowed areas as corroded spots. To improve the system's accuracy and pattern recognition capabilities, a shadow removal algorithm must be integrated with CMES. In this paper, two algorithms are compared for integration with CMES.
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