Adaptive Multi‐Task YOLO for Intelligent Transportation: Roadside Illegal Parking Detection and Parking‐Line Segmentation

Due to the mismatch between the rapid growth of cars and limited parking resources, illegal parking has become a critical problem in urban intelligent transportation systems. The core tasks for illegal parking judgement are object detection and parking line segmentation. The complex relationships among objects, sample imbalance, and the slender structure of parking lines make these tasks challenging. To address these issues, we propose an adaptive multi‐task YOLO network, termed Parking‐YOLO, which includes a feature extraction enhancement strategy, a Slide Loss‐based classification strategy, and an adaptive feature fusion strategy. The experimental results show that the first strategy improves detection accuracy by an average of 2.6% and enhances the understanding of complex relationships among objects. The second strategy reduces the TIDE classification error contribution by 54.3% by focusing more on a few hard samples. The last strategy increases the parking line segmentation accuracy by 1.4%, as it better adapts to the slender structure of parking lines. Additionally, the Parking‐YOLO outperforms mainstream object detection, semantic segmentation, and multi‐task learning models, and its inference speed meets real‐time requirements. Finally, we implement the roadside illegal parking judgement system, demonstrating its potential for intelligent transportation and roadside parking management.