基于 YOLOX 的小目标烟火检测技术 研究与实现

火灾是日常生活中最常见的社会灾害之一，会对人类的财产、生命安全造成巨大威胁，如何准 确而快速地发现小面积的烟火点并实时发出预警，对维护正常的社会生产具有重要意义。传统的烟火检测算法 通过识别图像的各种低维视觉特征如颜色、纹理等，进而判断烟火的位置，方法的实时性和精度较差。近些年 深度学习在目标检测领域的成就显著，各种基于深度神经网络的烟火检测方法层出不穷，但大部分深度学习模 型在小目标上的检测效果远不及大目标，而烟火检测任务需要在烟火面积很小时就做出及时地识别和预警，才 能避免火势扩大造成更大的经济损失。对此，基于 YOLOX 模型对激活函数和损失函数做出改进并结合数据增 强算法和交叉验证训练方法，实现了更好的小目标检测算法，在烟火检测数据集上获得了 78.36%的 mAP 值, 相比原始模型提升了 4.2%，并获得了更好的小目标检测效果。

Research and realization of small target smoke and fire detection technology based on YOLOX

 Fire is one of the most common social disasters in daily life, which will pose an enormous threat to human property and life safety. How to accurately and quickly identify small areas of smoke and fire and issue early warnings in real time is important for normal social production significance. The traditional smoke and fire detection algorithm identifies the location of smoke and fire based on various low-dimensional visual features of the images, such as color and texture, so it is of poor real-time performance and low accuracy. In recent years, deep learning has made remarkable achievements in the field of target detection, and various smoke and fire detection methods based on deep neural networks have sprung up one after another. In the case of small areas of smoke and fire, timely identification and early warning should be made to avoid greater economic losses caused by the expansion of the fire. In this regard,  based on the YOLOX model, the activation function and loss function were improved, and a superior small target detection algorithm was realized by combining the data augmentation algorithm and cross-validation training method, and the mAP value of 78.36% was obtained on the smoke and fire detection data set. Compared with the original model, it was enhanced by 4.2%, yielding a better effect of small target detection effect.