Abstract:

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.

Key words: multi-scale features, fire lane, occupation detection, YOLOv7, real-time monitoring