基于改进YOLOv7-x的多场景火灾识别算法

Multi-scene fire recognition algorithm based on improved YOLOv7-x

为解决发生火灾时传统的火灾报警器易受环境和空间的限制,出现检测精度下降的问题,提出1种基于改进YOLOv7-x的多场景火灾识别算法。首先,对火灾数据集进行多样化扩展,使其对任务场景更具适应性,再将扩展好的数据集进行数据增强以提升模型的鲁棒性;其次,在算法头部嵌入Coordinate Attention注意力机制,用来增强算法对火灾的专注力,并在算法主干引入ConvNeXtBlock结构,提升算法的精度;最后,使用Merge-NMS算法代替传统的NMS算法用来提升预测框位置的精准度。研究结果表明:改进YOLOv7-x算法相比于改进前的均值平均精确度mAP提高7.8个百分点,改进后的算法性能更优,可满足复杂场景下火灾精准检测的要求。研究结果可为智能化检测火灾提供技术支持。

In order to solve the problem that the traditional fire alarm is easily limited by the environment and space when a fire occurs,and the detection accuracy decreases,a multi-scene fire recognition algorithm based on improved YOLOv7-x was proposed.Firstly,the fire dataset was diversified and expanded to make it more adaptable to the task scene,and then the expanded dataset was enhanced to improve the robustness of the model.Secondly,the Coordinate Attention attention mechanism was embedded in the head of the algorithm to enhance the algorithm’s focus on fire,and the ConvNeXtBlock structure was introduced in the backbone of the algorithm to improve the accuracy of the algorithm.Finally,the Merge-NMS algorithm was used to replace the traditional NMS algorithm to improve the accuracy of the prediction box position.The results show that the value of improved YOLOv7-x algorithm is 7.8 percentage points higher than the value of mAP before improvement.The improved algorithm has better performance and can meet the requirements of accurate fire detection in complex scenes.The research results can provide technical support for intelligent fire detection.