FPGA-Accelerated YOLOX With Enhanced Attention Mechanisms for Real-Time Wildfire Detection on AAVs

Real-time wildfire detection is crucial for enabling prompt intervention and minimizing environmental and economic damages; however, deploying high-accuracy detection models on resource-constrained platforms like unmanned aerial vehicles (UAVs) presents significant challenges due to limitations in computational capacity and power availability. In this paper, we propose LCAM-YOLOX, an enhanced object detection framework that integrates a Layer-wise Channel Attention Module (LCAM) into the YOLOX architecture to improve detection accuracy while maintaining computational efficiency. The model is optimized for deployment on FPGA platforms through 8-bit integer quantization, facilitating efficient inference on devices with limited resources. We implement and evaluate the LCAM-YOLOX model on the Xilinx Kria KV260 FPGA platform, demonstrating that it achieves a quantized mean Average Precision (mAP) of 78.11%, outperforming other state-of-the-art models such as YOLOv3, YOLOv5, and YOLOX-m. Moreover, the LCAM-YOLOX model processes at 195 frames per second (FPS) using a single DPU core on the KV260, exceeding real-time processing requirements while consuming only 10.45 W of power, which translates to the highest performance per watt ratio among the tested platforms. These results highlight the suitability of the KV260 FPGA as an optimal choice for deploying high-performance, energy-efficient wildfire detection models on UAVs, enabling real-time monitoring in resource-constrained environments.