Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' Dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development.