Large-scale collection and annotation of sensitive facial data in real-world traffic scenarios face significant hurdles regarding privacy protection, temporal consistency, and high costs. To address these issues, this work proposes an integrated method specifically designed for sensitive information anonymization and semi-automatic image annotation (AIA). Specifically, the Nullface anonymization model is applied to remove identity information from facial data while preserving non-identity attributes including pose, expression, and background that are relevant to downstream vision tasks. Secondly, the Qwen3-VL multimodal foundation model is combined with the Grounding DINO detection model to build an end-to-end annotation platform using the Dify workflow, covering data cleaning and automated labeling. A traffic-sensitive information dataset with diverse and complex backgrounds is then constructed. Subsequently, the systematic experiments on the WIDER FACE subset show that Nullface significantly outperforms baseline methods including FAMS and Ciagan in head pose preservation and image quality. Finally, evaluation on object detection further confirms the effectiveness of the proposed approach. The accuracy achieved by the proposed method reaches 91.05%, outperforming AWS, and is almost identical to the accuracy of manual annotation. This demonstrates that the anonymization process maintains critical semantic details required for effective object detection.
Wang et al. (Sat,) studied this question.