FPGA-accelerated yolox with enhanced attention mechanisms for real-time wildfire detection on AAVS

Abstract

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 such as autonomous aerial vehicles (AAVs) presents significant challenges due to limitations in computational capacity and power availability. In this article, we propose layerwise channel attention module (LCAM)-YOLOX, an enhanced object detection framework that integrates an 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 AAVs, enabling real-time monitoring in resource-constrained environments.