Improved object detection network for pipeline leakage localization in the pneumatic system based on thermal images

The infrared thermal imaging method can effectively identify and locate leakage sites using the temperature characteristics of pneumatic system leakage. However, it is limited by the tiny dimensions of the objects, color shifts of varying background temperatures, and indistinctness of feature details. To address these issues, we integrate Omni-Dimensional Dynamic Convolution (ODDC), Squeeze-and-Excitation (SE) attention module, and Normalized Gaussian Wasserstein Distance (NWD) with You Only Look Once (YOLO) into a framework, named ODSW-YOLO, for the precise localization of leakage. Specifically, ODDC is employed to adjust the kernel parameters for improving the accuracy of detecting small targets. To reduce the influence of varying background temperatures, SE attention is adopted to extract key features. NWD is used to enhance the feature details by checking the small feature changes within the detection window. Finally, extensive experiments show that ODSW-YOLO improves the detection accuracy from 0.677 to 0.755, which surpasses the baseline model (YOLOv5).