标题： 通过使用迁移学习增强的深度学习模型在边缘计算中检测野火火焰和烟雾 显示英文标题

标题： Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models

摘要： 自主无人飞行器（UAV）结合边缘计算能力，使实时数据处理直接在设备上进行，显著降低了火灾检测等关键场景中的延迟。 本研究强调了迁移学习（TL）在提升对象检测器性能方面的重要性，用于识别火灾烟雾和火焰，特别是在有限数据集上训练时，并研究了TL对边缘计算指标的影响。 后者关注于TL增强的You Only Look Once（YOLO）模型在使用边缘计算设备时的推理时间、功耗和能耗方面的表现。 本研究利用空中火灾和烟雾基本（AFSE）数据集作为目标，Flame and Smoke Detection Dataset（FASDD）和Microsoft Common Objects in Context（COCO）数据集作为源数据集。 我们探索了一种两阶段级联的TL方法，使用D-Fire或FASDD作为初始阶段的目标数据集，AFSE作为后续阶段。 通过微调，TL显著提高了检测精度，达到了高达79.2%的平均精度（mAP@0.5），减少了训练时间，并提高了在AFSE数据集上的模型泛化能力。 然而，级联TL没有带来显著的改进，单独的TL也没有改善所评估的边缘计算指标。 最后，这项工作发现，当缺乏硬件加速时，YOLOv5n仍然是一个强大的模型，发现YOLOv5n处理图像的速度几乎比其较新的版本YOLO11n快一倍。 总体而言，结果证实了TL在提高对象检测器准确性方面的作用，同时也说明需要进一步的改进来提高边缘计算性能。 显示英文摘要

摘要： Autonomous unmanned aerial vehicles (UAVs) integrated with edge computing capabilities empower real-time data processing directly on the device, dramatically reducing latency in critical scenarios such as wildfire detection. This study underscores Transfer Learning's (TL) significance in boosting the performance of object detectors for identifying wildfire smoke and flames, especially when trained on limited datasets, and investigates the impact TL has on edge computing metrics. With the latter focusing how TL-enhanced You Only Look Once (YOLO) models perform in terms of inference time, power usage, and energy consumption when using edge computing devices. This study utilizes the Aerial Fire and Smoke Essential (AFSE) dataset as the target, with the Flame and Smoke Detection Dataset (FASDD) and the Microsoft Common Objects in Context (COCO) dataset serving as source datasets. We explore a two-stage cascaded TL method, utilizing D-Fire or FASDD as initial stage target datasets and AFSE as the subsequent stage. Through fine-tuning, TL significantly enhances detection precision, achieving up to 79.2% mean Average Precision (mAP@0.5), reduces training time, and increases model generalizability across the AFSE dataset. However, cascaded TL yielded no notable improvements and TL alone did not benefit the edge computing metrics evaluated. Lastly, this work found that YOLOv5n remains a powerful model when lacking hardware acceleration, finding that YOLOv5n can process images nearly twice as fast as its newer counterpart, YOLO11n. Overall, the results affirm TL's role in augmenting the accuracy of object detectors while also illustrating that additional enhancements are needed to improve edge computing performance.