  Early wildfire detection in remote and forest areas is crucial for minimizing
devastation and preserving ecosystems. Autonomous drones offer agile access to
remote, challenging terrains, equipped with advanced imaging technology that
delivers both high-temporal and detailed spatial resolution, making them
valuable 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 image
classification 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 to
develop a real-time image classification and fire segmentation model. It
presents a comprehensive investigation into hardware acceleration using the
Jetson Nano P3450 and the implications of TensorRT, NVIDIA's high-performance
deep-learning inference library, on fire classification accuracy and speed. The
study includes implementations of Quantization Aware Training (QAT), Automatic
Mixed Precision (AMP), and post-training mechanisms, comparing them against the
latest baselines for fire segmentation and classification. All experiments
utilize the FLAME dataset - an image dataset collected by low-altitude drones
during a prescribed forest fire. This work contributes to the ongoing efforts
to enable real-time, on-board wildfire detection capabilities for UAVs,
addressing speed and the computational and energy constraints of these crucial
monitoring systems. The results show a 13% increase in classification speed
compared to similar models without hardware optimization. Comparatively, loss
and accuracy are within 1.225% of the original values.
