AD-YOLO: adaptive framework for semi-supervised object detection in real-time traffic scenes

Abstract

In intelligent transportation systems, real-time detection demands high inference speed, making single-stage detectors the preferred choice. However, existing semi-supervised object detection (SSOD) frameworks suffer from pseudo-label noise and limited multi-scale adaptability when applied to such detectors. This paper presents AD-YOLO, a dual-level framework that tackles these issues from both the detector architecture and the SSOD pipeline. At the detector level, a normalization-guided attention module enables feature recalibration with zero extra parameters, and a CIoU-NWD hybrid loss incorporates the Wasserstein distance to suppress localization jitter. At the framework level, an Adaptive Teacher employs a category-aware dynamic threshold that selects pseudo-labels based on confidence percentiles, eliminating preset thresholds and calibration bias. This thresholding is assisted by a scale-aware dynamic augmentation mechanism, which uses teacher-generated pseudo-labels to identify small-object images and applies only weak augmentation to preserve their semantics. These two levels form a self-reinforcing loop. Experiments on BDD100K and TT100K show that with only 10% labeled data, AD-YOLO achieves 28.83% mAP at 142 FPS, demonstrating better performance compared with peer methods and exhibiting robustness in complex traffic scenes.