Fusion-Based Rear License Plates Detection and Recognition Considering Enlarged Prints

Computer vision-based License plate (LP) detection and recognition (LPDR) are fundamental tasks for vehicle management in logistics systems that provide foundations for the allocation of resources. Due to the practical limitations in logistics industrial parks, the vehicles need to be identified by recognizing the rear LPs in most cases. This induces a number of challenges such as stains, complex formats, and blocks induced by objects, which can easily lead to incorrect recognition. To resolve these issues, we noticed that the enlarged prints of LPs on the rear of certain types of vehicles are strongly complementary to the recognition of standard LPs. While the recognition of enlarged prints has not been studied in existing literature, this work proposes a novel method to detect and recognize both the standard and enlarged LPs, and strategically fuse the recognition results in both spatial and temporal dimensions. Experiments are conducted using both academic and industrial datasets, where the proposed method significantly outperforms a set of classic LPDR and optical character recognition (OCR) approaches, and is demonstrated to have great potential for application in real-world scenarios.