基于支持向量机的泄漏气体云团热成像检测方法

Gas leak detection technology based on thermal imaging has become an important means of oil and gas leakage detection because of its high detection efficiency and visibility. The conventional methods need personnel's subjective judge to trace gases from the video, so it is easy to lead miss and false detection. Therefore, this paper studies a thermal imaging detection algorithm of leaking gas clouds based on scale invariant feature transform (SIFT) and support vector machine (SVM), and uses the inter-frame difference method to screen the target region from the infrared image sequence. SIFT features of leaking gas and disturbance were extracted, respectively. SVM is used to identify the target in the candidate region and extract the leaking gas cloud. A database of 1000 typical target images was established for real complex scenes, including ethylene, methane, and other gas leakage images and disturbing images such as moving person, trees, and weeds. Through detection experiment, the classification accuracy of the proposed method for leaking gas clouds at 10150 m can reach 92.5%. The results show that this detection method can automatically eliminate the interference of other moving objects and effectively detect the leaking gas cloud.