Hybrid Dynamic Object Detection of Intelligent Park Based on Improved YOLOv5
In the context of urban security,the security problem of intelligent parks with dense human and vehicle has attracted in-creasing attention.Real-time detection of people and vehicles entering the park can provide important data reference for formulating reasonable evacuation strategies for people and vehicles in the park under emergency scenarios.To solve the accuracy problems caused by the complex environment,dense objects and occlusion of the current object detection during the occurrence of emergency events,as well as the rapidity problems caused by the number of model calculations,a hybrid object detection algorithm based on the improved lightweight YOLOv5 was proposed.Based on the original YOLOv5 model,the spatial pyramid pool structure spatial pyramid pooling-fast(SPPF)in the backbone network was improved to SimSPPF to improve the real-time performance of the model.By adding an extra edge to the mesoscale layer and introducing the channel attention mechanism coordinate attention(CA),the accuracy of the model in detecting the hybrid scene was improved.The experimental results show that compared with YOLOv5s,the algorithm not only maintains the detection speed at 142 frame/s but also improves the accuracy by 2.1％which meets the accuracy and real-time requirements of the hybrid dynamic detection of people and vehicles in the smart park.
smart parkemergency evacuationpeople and vehicles mixeddynamic target detectionimproved YOLOv5
万方数据
维普
