改进YOLOX-nano的火灾火焰烟雾检测

针对在检测火焰和烟雾的火灾检测过程中存在火灾初期小目标难以检测的情况, 本文提出了一种基于自然指数损失(eCIoU)的改进YOLOX-nano (ASe-YOLOX-nano)目标检测算法. 首先, 提出一种新的目标检测函数eIoU损失函数来替代传统IoU损失, 解决在检测小目标时预测框和真实框易出现无交集的情况, 及无法反应宽高影响等问题. 其次, 在网络模型中引入注意力模块, 在网络初期模糊定位目标位置, 提高网络后期对目标尤其是小目标检测的准确性. 此外, 本文还采用软池化空间金字塔池化结构提取不同尺寸的空间特征信息, 可以提升模型对于空间布局和物体变性的鲁棒性, 因此目标较小时也可以提取充足的特征, 采用Mosaic增强技术预处理数据集, 提升模型的泛化能力, 以此进一步提高网络性能. 通过目标数据集进行对比验证, 其结果显示, mAP指标达到70.07%, 比原模型提高了3.46%, 火焰的准确率达到84.66%, 烟雾的达到74.56%, FPS能够稳定在73, 相对于传统YOLOX-nano算法拥有更好的火灾检测能力.

Flame and Smoke Detection of Fires Based on Improved YOLOX-nano

As small targets in the early stage of a fire are difficult to detect during flames and smoke detection of fires, this study proposes an improved YOLOX-nano (ASe-YOLOX-nano) object detection algorithm based on natural exponential loss (eCIoU). Firstly, a new object detection function, the eIoU loss function, is proposed to replace the traditional IoU loss, which solves the problems of no intersection between the prediction box and the real frame in small target detection and the inability to react to the influence of width and height. Secondly, the attention module is introduced in the network model to vaguely locate the target position in the early stage of the network and improve the accuracy of the detection of targets, especially small targets, in the later stage of the network. In addition, the soft pooled spatial pyramid pooling structure is employed to extract spatial feature information of different sizes, which can improve the robustness of the model for spatial layout and object degeneration. In this way, sufficient features can be extracted when the target is small. Moreover, the Mosaic enhancement technology is used to preprocess the dataset to improve the generalization ability of the model for further improvement in network performance. The comparative verification of the target data set shows that the mAP index reaches 70.07%, which is 3.46% higher than that of the original model, and the model enjoys accuracy of flame and smoke detection of 84.66% and 74.56%, respectively, and a stable FPS of 73, which has better fire detection ability than the traditional YOLOX-nano algorithm.