Effective and Modest CNN: Plans for Fire Detection Systems

Fire detection is a critical task in safeguarding human life, property, and the environment, yet traditional methods often struggle with delayed responses and high false alarm rates. In this study, we propose a deep learning-based framework that leverages convolutional neural networks (CNNs) to automatically identify fire in digital images. The CNN is trained on a diverse dataset comprising both fire and non-fire scenes, enabling it to learn discriminative visual patterns such as color intensity, irregular flame contours, and dynamic texture characteristics. Unlike conventional rule-based or handcrafted feature approaches, our method allows the network to autonomously extract and optimize features, improving generalization across varied scenarios. The performance of the proposed system was rigorously evaluated on an independent test set, with results demonstrating strong classification accuracy, precision, and recall. These outcomes confirm the robustness of our approach in distinguishing fire from challenging non-fire instances, such as sunsets, artificial lighting, or objects with flame-like hues, thereby minimizing false positives. Due to its efficiency and adaptability, the proposed framework can be deployed in multiple real-world contexts. Potential applications include early fire warning systems in residential and industrial environments, intelligent surveillance for public safety, and large-scale monitoring of wildfire-prone regions. Overall, this work highlights the effectiveness of CNN-based methods for real-time fire detection and contributes to advancing intelligent safety and hazard management technologies.