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PSND: A Robust Parking Space Number Detector

It is necessary to detect the license plate and the parking space number in autonomous driving. Due to the complex environment of the parking space, it is challenging and interesting in parking space number detection. This paper proposes a robust Parking Space Number Detector (PSND), which enhances the Differentiable Binarization (DB) model by introducing a cascaded feature enhancement module and context attention block. Our method has better feature extraction ability and a better detection effect for long text than the DB model. Meanwhile, we collected and annotated a dataset containing 9000 parking space number images to train and test our model. Extensive experiments demonstrate that our method achieves better or competitive performance in terms of accuracy on various standard benchmarks, including MSRA-TD500, ICDAR2015, CTW1500, and Total-Text datasets while maintaining the real-time detection speed. The high recognition accuracy makes it possible for the model to be practically applied.