  The burgeoning navigation services using digital maps provide great
convenience to drivers. Nevertheless, the presence of anomalies in lane
rendering map images occasionally introduces potential hazards, as such
anomalies can be misleading to human drivers and consequently contribute to
unsafe driving conditions. In response to this concern and to accurately and
effectively detect the anomalies, this paper transforms lane rendering image
anomaly detection into a classification problem and proposes a four-phase
pipeline consisting of data pre-processing, self-supervised pre-training with
the masked image modeling (MiM) method, customized fine-tuning using
cross-entropy based loss with label smoothing, and post-processing to tackle it
leveraging state-of-the-art deep learning techniques, especially those
involving Transformer models. Various experiments verify the effectiveness of
the proposed pipeline. Results indicate that the proposed pipeline exhibits
superior performance in lane rendering image anomaly detection, and notably,
the self-supervised pre-training with MiM can greatly enhance the detection
accuracy while significantly reducing the total training time. For instance,
employing the Swin Transformer with Uniform Masking as self-supervised
pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an
improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin
Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an
AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the
original 280. In conclusion, the proposed pipeline, with its incorporation of
self-supervised pre-training using MiM and other advanced deep learning
techniques, emerges as a robust solution for enhancing the accuracy and
efficiency of lane rendering image anomaly detection in digital navigation
systems.
