Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images

The periodic hollowing inspection of the existing building exterior wall is crucial for public safety and building energy conservation. Due to its non-destructive and intuition advantage, the infrared thermal detection is proposed to be an ideal survey method. However, much manual participation is required to distinguish the hollowing flaw relying on empirical judgment, and a heavy burden comes up when large-area diagnosis required. In order to improve the efficiency of hollowing detection, this investigation developed the Canny algorithm to realize the automatic processing using the computer instead of manual judgment. At first, reasonable pieces of setting advice were given to get more clear hollowing region contours with the final recognition outcome comparison of different processing methods for each step. Besides, it was found that the hollowing contour gradient values are lower and exist in a short interval, and the segmentation threshold value was critical in the Canny edge-detection algorithm, which highly restricted the speed of processing large amounts of infrared images. To improve the efficiency of thermal image recognition, a threshold selection method based on the local maximum inter-class variance algorithm was introduced into the Canny edge-detection algorithm. Compared with Sobel, Roberts, Prewitt, and LoG, the proposed algorithm presented a better performance in the identification of hollowing edge contour according to the verification based on three cases. It revealed that the improved Canny edge-detection algorithm was effective and efficient, which could not only eliminate the influence of subjective factors but also achieve full-automatic and batch processing.