In the era of “dual carbon goals” and safe development, industrial gas leakage detection has become an important issue of widespread concern both domestically and internationally due to its involvement in economic resources, ecological environment, and production safety. Gas passive infrared imaging is considered as an effective tool for detecting leaks due to its dynamic and intuitive characteristics, as well as the ability to perform non-contact large-scale telemetry. In this technology, intelligent detection of gas targets and leakage quantification are two core research hotspots, and they will remain two challenges for a long time to come. In view of this, a review is conducted on these two aspects of research. Firstly, the principle of passive infrared imaging gas detection technology is analyzed, and the key factors that affect imaging detection results and their forms of action are explored. Secondly, the gas intelligent detection algorithms are classified and sorted out according to different computer vision tasks such as image recognition, video classification, object detection, and image segmentation. Thirdly, the measurement methods of column density, path concentration and leakage rate in the gas quantification task are introduced respectively, and the importance of uncertainty analysis to the quantification results is emphasized. Finally, some potential solutions are provided for the problems in the research of intelligent gas detection and quantification, and the future research directions of gas passive infrared imaging technology are prospected.