Abstract: To address the issues of unsatisfactory intuitiveness and real-time performance, as well as the high false-alarm rate in current automated infrared imaging gas-leakage detection methods, a real-time leakage-detection model named Gas-Seg, which is based on the improved YOLOv5-Seg, is proposed. Gas-Seg adopts a leakage gas cloud-segmentation method, thus achieving a low false-positive identification and an intuitive display of the leakage areas. To enhance the model's ability to learn the key features of the leaking gas, a convolutional block attention module was used to merge the spatial and channel features. Atrous spatial pyramid pooling was applied to extract the multi-scale features of gas clouds, thereby improving the accuracy of gas cloud identification. Additionally, the use of the C3Ghost module reduced the model's parameters, consequently enhancing its inference speed. Finally, an auxiliary validation method was introduced to eliminate false alarms from stationary areas, thereby effectively reducing false alarms in single-frame detections. Ultimately, the Gas-Seg model achieved 93.5% and 66.5% improvements in the mAP@0.5 and mAP@0.5:0.9 metrics, respectively, which correspond to improvements by3.7% and 2% compared with YOLOv5-Seg, respectively. In ethylene-gas detection experiments at distances of 10 m with leakage rates of 0.75 and 1.5 L/min, the warning accuracies reached 84.4% and 99.7% respectively. Furthermore, the inference speed reached 51 frames per second, thus demonstrating its potential for real-time detection.