Conventional contact methane leak sensors suffer from a small detection range and low efficiency， but machine vision algorithms combined with non-contact infrared thermal imaging can make infrared methane instance segmentation possible at long distances and large ranges. This is a significant advantage for improving methane detection efficiency and ensuring personnel safety. However， the segmentation performance of infrared methane instances is limited by such problems as blurred contour and low contrast between the leaking methane gas and the background， and it can be affected by atmospheric flow factors. In response to these problems， an adaptive spatial information regulation and feature alignment network （AFNet） is proposed to segment infrared instances of methane leakage. First， to enhance the model’s feature extraction， an adaptive spatial information regulation module is proposed to endow the backbone network with adaptive weights for different scale residual blocks， which enrich the feature space extracted by the model. Second， to meet the requirements of foreground target positioning detection and contour segmentation under complex methane gas contours， a weighted bidirectional pyramid is designed to reduce the diffusion， loss of spatial location， and instance edge information in low-level features， which are caused by the top-down propagation of the feature pyramid. Finally， a prototype feature alignment module is designed to capture the semantic relationships between long-distance gas features， enriching the semantic information of the prototype and improving the quality of generated target masks to improve the methane instance segmentation accuracy. Experimental results show that the proposed AFNet model achieves AP50@95 and AP50 quantitative segmentation accuracies of 42.42% and 92.18%， which are improved by 9.79% and 6.18% compared with the original Yolact， respectively. In addition， the inference speed achieves 36.80 frames/s and meets the requirements of methane leakage segmentation. The experimental results validate the effectiveness and engineering practicality of the algorithm proposed for infrared methane leakage segmentation.