Ensembling Deep Learning And CIELAB Color Space Model for Fire Detection from UAV images

Wildfires can cause significant damage to forests and endanger wildlife. Detecting these forest fires at the initial stages helps the authorities in preventing them from spreading further. In this paper, we first propose a novel technique, termed CIELAB-color technique, which detects fire based on the color of the fire in CIELAB color space. We train state-of-art CNNs to detect fire. Since deep learning (CNNs) and image processing have complementary strengths, we combine their strengths to propose an ensemble architecture. It uses two CNNs and the CIELAB-color technique and then performs majority voting to decide the final fire/no-fire prediction output. We finally propose a chain-of-classifiers technique which first tests an image using the CIELAB-color technique. If an image is flagged as no-fire, then it further checks the image using a CNN. This technique has lower model size than ensemble technique. On FLAME dataset, the ensemble technique provides 93.32% accuracy, outperforming both previous works ( accuracy) and individually using either CNNs or CIELAB-color technique. The source code can be obtained from https://github.com/CandleLabAI/FireDetection.