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Early wildfire detection in remote and forest areas is crucial for minimizingdevastation and preserving ecosystems. Autonomous drones offer agile access toremote, challenging terrains, equipped with advanced imaging technology thatdelivers both high-temporal and detailed spatial resolution, making themvaluable assets in the early detection and monitoring of wildfires. However,the limited computation and battery resources of Unmanned Aerial Vehicles(UAVs) pose significant challenges in implementing robust and efficient imageclassification models. Current works in this domain often operate offline,emphasizing the need for solutions that can perform inference in real time,given the constraints of UAVs. To address these challenges, this paper aims todevelop a real-time image classification and fire segmentation model. Itpresents a comprehensive investigation into hardware acceleration using theJetson Nano P3450 and the implications of TensorRT, NVIDIA's high-performancedeep-learning inference library, on fire classification accuracy and speed. Thestudy includes implementations of Quantization Aware Training (QAT), AutomaticMixed Precision (AMP), and post-training mechanisms, comparing them against thelatest baselines for fire segmentation and classification. All experimentsutilize the FLAME dataset - an image dataset collected by low-altitude dronesduring a prescribed forest fire. This work contributes to the ongoing effortsto enable real-time, on-board wildfire detection capabilities for UAVs,addressing speed and the computational and energy constraints of these crucialmonitoring systems. The results show a 13% increase in classification speedcompared to similar models without hardware optimization. Comparatively, lossand accuracy are within 1.225% of the original values.

Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks