基于多尺度特征的消防车道占用检测

Fire lane occupancy detection based on multi-scale features

为解决消防车道占用预警的智能检测问题,引入区域入侵原理,提出基于YOLOv7的轻量化消防车道占用预警方法。首先,以YOLOv7模型为基础,构建消防车道区域入侵研究框架,将实地调研与公开数据集相结合,形成包含消防车道与车辆检测的图像数据集;其次,采用感受野块模块替换空间金字塔池化跨阶段部分卷积,同时,添加SimAM注意力模型,提高网络多尺度特征提取和融合效果;然后,运用Slim-Neck结构减小模型的计算量和参数量;最后,通过PyQt5部署交互式界面设计,在陕西省西安市某小区进行实地算法验证。结果表明:模型识别消防车道和车辆的准确率均达到80%以上;与原模型相比,改进后的模型参数量减少20.5%,浮点计算量降低11.3%,检测速度提高42.4%,达到48.6帧/s。

To solve the intelligent detection problem of fire lane occupancy warning,a lightweight early warning approach based on YOLOv7 was proposed by introducing the principles of area intrusion.Firstly,a research framework for detecting fire lane area intrusions was devised,utilizing the YOLOv7 model.This was accompanied by the compilation of an image dataset that encompassed fire lanes and vehicle detection,sourced from both field investigations and open datasets.Subsequently,the spatial pyramid pooling's multi-stage partial convolution was substituted with a receptive field block module,and the SimAM attention model was incorporated to enhance the network's capability in multi-scale feature extraction and fusion.Furthermore,the Slim-Neck architecture was implemented to minimize the model's computational requirements and parameter count.The interactive interface was then designed and implemented using PyQt5.The algorithm was subsequently validated in a community located in Xi'an,Shaanxi Province.The results show that the accuracy of the model to identify fire lanes and vehicles is over 80%.Compared with the original model,the improved model reduces the number of parameters by 20.5%,the floating-point calculation by 11.3%,and the detection speed by 42.4%to 48.6 f/s.This promotes the development of intelligent detection technology for fire lane occupancy.