基于 YOLOv5s 的轻量化森林火灾检测算法研究

针对过去无人机搭载嵌入式设备巡检森林火灾精确率低、适应性差和软硬件限制高等问题，提 出一种基于 YOLOv5s 的轻量化森林火灾目标检测算法。通过将 YOLOv5s 的骨干网络替换为轻量化网络 Shufflenetv2，并以通道重组的思想，让骨干网络对图片信息的提取效率变得更快，在保持网络精度的同时保证 检测速度；接着在 Backbone 与 Neck 的连接处加入为轻量化网络设计的 CA 位置注意力模块，可将图片不同的 位置信息聚合到通道中，使被检对象关注度得以提高；最后在预测部分使用 CIOU 损失函数，能够更好的优化 矩形框的长宽比和更快加速模型收敛。算法部署在嵌入式系统 Jetson Xavier NX 上的结果显示，改进后的网络 模型大小与对比实验方法相比，最多减少了 98%，准确率(Precision)达到 92.6%，精确率(AP)达到 95.3%，帧率 (FPS)提升到 132 帧每秒，能满足在白天、黑夜或视野良好等情况下对森林火灾的实时性预防与检测，并具有 良好的准确率和鲁棒性。

Research on lightweight forest fire detection algorithm based on YOLOv5s

A new algorithm for light-weight forest fire object detection was proposed based on YOLOv5s to address the low accuracy, poor flexibility, and high software and hardware limitations of the previous UAV-embedded equipment for forest fire inspection. The proposed algorithm replaced the backbone of YOLOv5s with the light-weight network Shufflenetv2, employed the idea of channel recombination to improve the speed of the backbone network in picture information extraction, and maintained both high accuracy and fast detection speed. Then, a coordinate attention (CA) positional attention module specially designed for light-weight network was added to the connection between Backbone and Neck, which could aggregate different position information of pictures into the channel, thus improving the attention of the detected object. Finally, the CIOU loss function was utilized in the prediction part to better optimize the ratio of length to width of the rectangular frame and accelerate the model convergence. The results of the algorithm deployed on Jetson Xavier NX show that compared with the Faster-RCNN, SSD, YOLOv4, and YOLOv5s experimental methods, the improved network model size was reduced by up to 98%, increasing the precision to 92.6%, accuracy rate to 95.3%, and FPS to 132 frames/s. It can effectively achieve the real-time prevention and detection of forest fire in daylight, darkness, or good visibility, exhibiting good accuracy and robustness.