Early forest flame and smoke detection based on improved feature extraction module with enhanced image processing inspired by YOLOV7

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Abstract
Background: The forest environment is intricate and dynamic, with its depiction influenced by various factors such as geographical location, weather conditions, and capture angles. Relying solely on flame or smoke is insufficient for precise fire information. Aims: This paper proposes a method for accurate detection on forest flame and smoke based on improved feature extraction module with enhanced image processing. Methods: We a fusion-guided filtering image processing method and flame segmentation strategy to augment the quality of dataset. Additionally, an outstanding extraction backbone, incorporating ghost modules and decoupled fully connected (DFC) attention modules, is developed to increase the model’s receptive field. Furthermore, the ELAN-S neck with SimAM attention mechanism is introduced to fuse features from the backbone network, facilitating the extraction of shallow and deep-level semantic information. Key results: Compared to YOLOV7, our model demonstrates superior performance with a 5% increase in mean average precision (mAP), a 4.3% increase in average precision for small objects (APS), and a 3–4% enhancement in other metrics. Conclusions: The proposed model achieves a good balance between detection speed and detection accuracy. The improved model performs well in real forest fire detection scenarios. Implications: In the early forest fire detection, the model considers both flame and smoke information to describe the fire situation, and effectively combines the semantic information of both for fire warning.

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