Abstract
Conveying systems play an essential role in the continuous horizontal transportation of raw materials in mining sites. Regular inspections of conveyor system structures and their components, especially idlers, are essential for proper maintenance. Traditional inspection methods are labor-intensive and hazardous; therefore, robot-based thermography can be considered a quality assessment tool for the precise detection and localization of overheated idlers in opencast mining sites. This paper proposes an infrared image processing pipeline for the automatic detection and analysis of overheated idlers. The proposed image processing pipeline can be used for the identification of significant temperature anomalies such as hotspots and hot areas in infrared images. For the identification of such defects in idlers, firstly, the histogram of captured infrared images was analyzed and improved through the pre-processing stages. Afterward, the location of thermal anomalies in infrared images was extracted. Finally, for the validation of segmentation results, the shapes and locations of segmented hot spots were compared with RGB images that were synchronized by captured infrared images. A quantitative evaluation of the proposed method for the condition monitoring of belt conveyor idlers in an open-cast mining site shows the applicability of our approach.