Dual-stream information adaptive fusion network for smoke detection in the complex environments of steel mills

In the intricate environments of steel mills, traditional target detection algorithms relying on a single RGB image can hinder smoke detection. This is due to significant light variations under diverse lighting conditions, the evolving morphology of smoke, and the presence of steam, sparks, and similar gases in the surroundings. With no dedicated algorithm for identifying smoke with its distinctive morphology and color, as produced in steel mill operations, this paper introduces a novel approach inspired by the structure of YOLOv9, termed DSAIF-Net—a dual-stream information network. DSAIF-Net leverages adaptive feature fusion from RGB and optical flow images to detect smoke amidst complex lighting and environmental challenges.To enhance feature fusion—both static and dynamic—a weighted bidirectional transfer feature adaptive fusion module (ROBFM) is proposed. ROBFM intelligently merges the original RGB image and the optical flow image, elevating model accuracy while reducing misidentification and false alarms. Additionally, to streamline model parameters and computational load without compromising accuracy, a lightweight attention mechanism (EMiRMBA) is incorporated. To validate the model's efficacy, we curated a comprehensive dual-stream information smoke detection dataset from surveillance videos across various steel plant scenarios. The results show that our proposed method achieves superior detection accuracy with the lowest dataset requirements, with an accuracy of 0.960, a recall of 0.908, and a more complete coverage of prediction frames, as well as the lowest miss and false detection rates. Moreover, the size of our model is reduced to 85% of the original network model, which affirms its efficiency and scalability.