UKD-TEAD: An Unsupervised Knowledge Distillation Framework for Detecting Anomalies in Traffic Equipment With Various Aspect Ratios

The inefficient capture of equipment anomalies has impeded the effective training of models for detecting anomalies in various traffic equipment (TE). This article proposes an unsupervised knowledge distillation for traffic equipment anomaly detection (UKD-TEAD), eliminating the need for numerous annotations and ensuring the applicability to various equipment anomalies. First, three specialized detection heads based on different object category aspect ratio distributions are designed, to detect multiscale objects with high precision in complex traffic scenes. Second, a teacher-student model, grounded in hierarchical knowledge distillation, is developed to mitigate the critical feature loss associated with the small size of cropped regions of interest (ROIs). By performing knowledge distillation at different depths of the network, the student network effectively learns the representation capabilities of the teacher network on multiple scale feature layers, thereby improving the anomaly detection performance. Finally, to validate the proposed unsupervised anomaly detection framework, a target detection dataset and an unsupervised anomaly detection dataset were constructed based on traffic inspection data. Experimental results show that the proposed method achieves an mAP@0.5 of 0.862 for TE detection, while the mean area under the curve (mAUC) for anomaly detection reaches 0.857.