End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
This work addresses a specific challenge in license plate recognition systems for applications like on-road monitoring, but it is incremental as it builds on existing detection methods with targeted improvements.
The paper tackles the problem of detecting small and oblique license plates in real-world scenarios by proposing an end-to-end trainable network that uses vehicle-plate relation mining to localize plates in a coarse-to-fine scheme, achieving improved detection performance as verified through extensive experiments.
License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particular, small-sized and oblique license plates, mainly caused by the distant and mobile camera, are difficult to detect. In this work, we propose a novel and applicable method for degraded license plate detection via vehicle-plate relation mining, which localizes the license plate in a coarse-to-fine scheme. First, we propose to estimate the local region around the license plate by using the relationships between the vehicle and the license plate, which can greatly reduce the search area and precisely detect very small-sized license plates. Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates. Moreover, the whole network can be trained in an end-to-end manner. Extensive experiments verify the effectiveness of our proposed method for small-sized and oblique license plates. Codes are available at https://github.com/chensonglu/LPD-end-to-end.