Air Leak Detection Using Sobel-Enhanced YOLO Algorithm from Infrared Images

Air leakage significantly contributes to energy loss in commercial and residential building envelopes. For the next generation of sustainable building construction, enhancing energy efficiency through effective air leak detection is paramount. Traditional air leak tests rely on intrusive methods and manual detection, which lack precision and speed in locating leaks. The rapid advancements of artificial intelligence offer tremendous opportunities to revolutionize these detection methods. This paper introduces an automatic real-time detection method based on the integration of an edge detection Sobel block with the standard real-time object detector YOLO (You Only Look Once) frame-work. It transforms single-channel infrared (IR) images into three channels that provide additional edge information while retaining the original data. It has been implemented in various YOLO versions to demonstrate its effectiveness with training on a custom dataset primarily collected from a test chamber experiment. The results show that the proposed enhanced models achieve fast and accurate detection with compact model size, making it easily implementable in handheld IR cameras or drone-mounted systems for both indoor and outdoor applications. This allows a significant advancement in the non-intrusive and precise detection of air leaks.