On-Street Parking Space Detection Using YOLO Models and Recommendations Based on KD-Tree Suitability Search

Abstract

Unlike the detection of marked on-street parking spaces, detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking. In urban cities with heavy traffic flow, these challenges can result in traffic disruptions, rear-end collisions, sideswipes, and congestion as drivers struggle to make decisions. We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search. Lightweight versions of YOLOv5, YOLOv7-tiny, and YOLOv8 with different architectures are trained. Among the models, YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89, which was selected for validation using k-fold cross-validation on our dataset. The Low variance and standard deviation recorded across folds indicate the model’s generalizability, reliability, and stability. Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5, 67.2 for YOLOv7-tiny, and 67.0 for YOLOv8. The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS. These models can be efficiently deployed for real-time applications due to their high FPS, inference speed, and lightweight nature. In comparison with other state-of-the-art models, our models outperform them, further demonstrating their effectiveness.