Infrared Image Detection Method of Gas Leakage Based on Improved YOLOv8

To solve the problems of high missed detection rate, frequent misjudgment and difficult model fitting in infrared detection of gas leakage, a YOLOv8-CBAM method with increasing the CBAM attention mechanism module and optimizing the loss function to Focaler-loU was proposed to improve the accuracy and stability of infrared detection of gas leakage. Experimental results show that the detection accuracy of the improved YOLOv8-CBAM convolutional neural network is significantly better than that of other mainstream object detection network models, and the average detection accuracy of the test set is as high as 98.82%. The average number of detection frames per second is 109.9frames, which meets the real-time and accuracy requirements of actual gas leakage detection tasks.