Event-level illegal parking detection using YOLOv8 and SSD models

Abstract

Unauthorized parking in urban areas results in traffic congestion and leads to the inefficient use of road infrastructure. This study presents an event-level illegal parking detection framework that combines deep learning–based object detection with region-based temporal logic to distinguish transient stops from actual parking violations. A comparative analysis of YOLOv8s and single shot multibox detector (SSD) is conducted using real-world surveillance footage spanning high traffic density and poor lighting conditions. Unlike traditional approaches that rely on hardware-intensive multi-object tracking, our method utilizes a 30-second stationarity threshold within designated regions of interest (ROIs) to detect violations. Experimental results indicate that YOLOv8s outperforms SSD in enforcement reliability, achieving a Recall of 1.00 on a limited-scale evaluation set of 12 confirmed violation events and an F1-score of 0.89, compared with SSD’s Recall of 0.67 and F1-score of 0.77. Moreover, YOLOv8s achieves an inference speed of up to 97 frames per second (FPS), well beyond real-time requirements. These results indicate strong potential for real-time illegal parking detection under urban surveillance conditions.