Detection of vacant parking space in adverse weather Based on Improved YOLO Network Model

The increasing urban population in Delhi has exacerbated the prevailing parking challenges, and traffic congestion. In response, governmental efforts to implement "Smart Parking" initiatives have been initiated; however, existing solutions encounter constraints. This paper presents a pioneering Optimal Parking Allocation Model (OPSAM) leveraging the YOLO (You Only Look Once) detection algorithm to revolutionize parking management in Delhi. OPSAM signifies a paradigmatic shift from driver-side parking search to system-side allocation, thereby optimizing parking space allocation. Benefiting from YOLO's rapid detection speed and suitability for real-time object detection, this study employs the YOLO v3 network for vehicle and the occupancy status of parking space detection in parking lots. Experimental findings demonstrate the method's efficacy in enhancing vehicle and parking space detection accuracy while mitigating neglected detection rates.