改进YOLOX-m的火焰检测方法研究

Research on Flame Detection Method Based on Improved YOLOX-m

摘要:

如今火灾自动报警技术已逐步朝着智能化、网络化和自动化的方向发展，然而，目前的火焰实时检测技术存在火焰实时检测精度低和网络计算参数量大等问题。针对以上问题，对YOLOX-m目标检测模型进行研究，提出改进YOLOX-m的轻量级火焰检测模型。通过将主干网络CSPDarknet-53替换为ShuffleNetV2，在降低计算量的同时提高网络精度，在ShuffleNetV2结构中插入RFB模块扩大感受野，在保持分辨率和精确定位检测目标的同时提升检测大目标的能力，将Neck部分的上采样替换为Pixel Shuffle以降低特征损失，为使网络能够关注到更关键的信息，增加注意力机制CBAM，从而提高模型整体性能。经过算法优化和实验测试，改进模型比YOLOX-m模型精度提高了2.87个百分点，参数量减少37.9%，计算量降低30.7%。改进模型成功应用于森林火灾、城市火灾等实际场景，通过对比可以相对更加精确地检测火焰。

Abstract:

Nowadays, automatic fire alarm technology has been gradually advancing towards intelligence, networking, and automation. However, the current real-time flame detection techniques suffer from low detection accuracy and high computational parameters. To address these issues, this study focuses on the YOLOX-m object detection model and proposes an improved lightweight fire detection model. The proposed model improves the YOLOX-m model by replacing the CSPDarknet-53 backbone network with ShuffleNetV2, which reduces computational load while improving network accuracy. Additionally, the RFB module is inserted into the ShuffleNetV2 structure to increase the receptive field, enhancing the detection capability for large objects while maintaining resolution and precise localization. The upsampling in the Neck part is replaced with Pixel Shuffle to minimize feature loss. Furthermore, the CBAM attention mechanism is incorporated to enable the network to focus on crucial information, thus improving overall model performance. Through algorithm optimization and experimental testing, the improved model achieves a 2.87% increase in accuracy compared to the YOLOX-m model, with a reduction of 37.9% in parameters and a 30.7% decrease in computational load. The improved model has been successfully applied to real-world scenarios such as forest fires and urban fires. By comparison, flames can be detected relatively more accurately.