Improved YOLOX Fire Scenario Detection Method

Abstract

Considering the problems of existing target detection model difficulty for use in complicated fire scenarios and few detection targets, an improved YOLOX fire scenario detection model was introduced, to realize multitarget detection of flame, smoke, and persons: firstly, a light attention module, for improving the overall detection performance of the model; secondly, the channel shuffle technique was employed, for increasing the communication ability between channels; and finally, the backbone channel was replaced with a light transformer module, for enhancing the capture ability of the backbone channel for global information. As shown in the experiment with self-developed fire dataset, mAP of T-YOLOX increased by 2.24% as compared with the benchmark model (YOLOX), and the detection accuracy was significantly improved as compared with that of CenterNet and YOLOv3, showing the effectiveness and advantages of the algorithm.