Infrared Imaging Detection for Hazardous Gas Leakage Using Background Information and Improved YOLO Networks

Hazardous gas leakage in the petrochemical industry frequently results in major incidents. A significant challenge arises due to the limitations of the current gas plume target feature extraction and identification techniques, which reduce the automated detection capabilities of remote monitoring systems. To address this, we propose BBGFA-YOLO, a real-time detection method leveraging background information and an improved YOLO network. This approach is designed specifically for the infrared imaging of gas plume targets, fulfilling the requirements of visual remote monitoring for hazardous gas leaks. We introduce a synthetic image colorization method based on background estimation, which leverages background estimation techniques to integrate motion features from gas plumes within the synthesized images. The resulting dataset can be directly employed by existing target detection networks. Furthermore, we introduce the MSDC-AEM, an attention enhancement module based on multi-scale deformable convolution, designed to enhance the network’s perception of gas plume features. Additionally, we incorporate an improved C2f-WTConv module, utilizing wavelet convolution, within the neck stage of the YOLO network. This modification strengthens the network’s capacity to learn deep gas plume features. Finally, to further optimize the network performance, we pre-train the network using a large-scale smoke detection dataset that includes reference background information. The experimental results, based on our self-acquired gas plume dataset, demonstrate a significant improvement in detection accuracy with the BBGFA-YOLO method, specifically achieving an increase in the average precision (AP50) from 74.2% to 96.2%. This research makes a substantial contribution to industrial hazardous gas leak detection technology, automated alarm systems, and the development of advanced monitoring equipment.