A Method for Electric Bicycle Detection in Elevator Based on YOLOv8 Network Model
The entry of electric bicycles into elevator for charging poses significant safety risks to residents and may lead to fire hazards and life safety issues.To solve this problem,an automatic detection method for electric bicycles inside elevators was put forward in this study based on deep learning techniques.The YOLOv8 network model was used as the core detection model and was trained and tested using a constructed dataset.Experimental results showed that the model could accurately identify electric bicycles entering elevators even when partially obstructed,highlighting the effectiveness and robustness of the YOLOv8 network model in various scenarios.Furthermore,comparative experiments with the YOLOv5 network model confirmed the superiority of the YOLOv8 network model.This approach not only enhanced safety management efficiency,but also provided timely warnings to significantly improve elevater passenger safety.In comparison to traditional manual detection method and video surveillance system,the proposed electric bicycle detection method offered real-time image processing capabilities and precise target identification with broad practical value and application prospect.
elevator safetyelectric bicycle detectionYOLOv8object detectionnetwork model
万方数据
维普
