An infrared gas detection algorithm based on weak supervision

Infrared image recognition technology has a wide range of applications in the field of gas detection. Unlike visible light images, gas detection in infrared images is relatively difficult due to the lack of clear contrast and the relative blurriness of gas targets. This paper proposes a weakly supervised distillation network to address the issue of low detection accuracy of gas regions in infrared images in complex scenes. This method mainly generates accurate heatmaps as pseudo labels by utilizing complex class activation mappings; Using pseudo labels to train the specialized model proposed in this article, more accurate heat map results are generated, and finally the heat map results are fused with the foreground obtained based on background difference method to reduce false positives in combustible gas detection results. The experimental results show that the proposed method has high accuracy in various scenarios, and the model can efficiently run in embedded systems, effectively solving the problem of infrared gas recognition in complex scenarios.