Thermal defect detection aims to identify overheated areas of electric accessory with the help of infrared imaging technology. In this paper, we propose a thermal defect segmentation method based on saliency constraint. Specifically, we first design a convolutional neural network for infrared image classification, the thermal ones of which are then denoised and enhanced by image preprocessing; Next, the modified K-means clustering algorithm is utilized for region segmentation, which splinters infrared images as environment area, normal area and thermal area; Finally, we perform saliency detection on infrared images to obtain approximate region of temperature anomaly, and the overheated area is likewise segmented based on the modified K-means clustering algorithm, which is subsequently used to revise the thermal area segmented based on enhanced images to satisfy saliency constraint. Experimental results suggest that our method can improve the diagnostic efficiency of infrared images and realize the precise positioning of thermal defects, which outperforms the state-of-the-arts.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.