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ABSTRACT
This article introduces an innovative approach, the ATT Squeeze U-Net, designed to tackle the critical issue of forest fires through advanced computer vision techniques. Leveraging an asymmetric encoder-decoder U-shape architecture, this model incorporates Squeeze Net, an Attention U-Net, and sophisticated mechanisms to discern and delineate forest fire regions effectively.
The core innovation lies in the ATT Squeeze U-Net’s ability to function as both an extractor and discriminator of forest fires. The attention mechanism, integrated with Attention Gate (AG) units within the U-shape structure, accentuates relevant features while suppressing irrelevant content. This approach significantly optimizes the forest fire segmentation process, even with fewer parameters, ensuring accuracy and real-time processing.
The modifications made to the classical Squeeze Net are pivotal. By substituting classical convolution layers with depth-wise convolution and introducing Channel Shuffle operations within the Fire module, the model achieves enhanced feature communication and efficiency. This modified Squeeze Net replaces the encoder in the Attention U-Net and integrates a DeFire module into the decoder, further refining the up-sampling process.
Additionally, the article delves into a novel classification framework within the ATT Squeeze U-Net. It repurposes a segment of the encoder to classify identified fire areas accurately, distinguishing between true and false positives. The meticulous selection of output feature maps from encoder layers further optimizes fire recognition.
The article underscores the importance of early forest fire detection, mitigating the destructive impact on ecology and the economy.
