4 research outputs found
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Benefiting from the rapid development of convolutional neural networks, the
performance of car license plate detection and recognition has been largely
improved. Nonetheless, challenges still exist especially for real-world
applications. In this paper, we present an efficient and accurate framework to
solve the license plate detection and recognition tasks simultaneously. It is a
lightweight and unified deep neural network, that can be optimized end-to-end
and work in real-time. Specifically, for unconstrained scenarios, an
anchor-free method is adopted to efficiently detect the bounding box and four
corners of a license plate, which are used to extract and rectify the target
region features. Then, a novel convolutional neural network branch is designed
to further extract features of characters without segmentation. Finally,
recognition task is treated as sequence labelling problems, which are solved by
Connectionist Temporal Classification (CTC) directly. Several public datasets
including images collected from different scenarios under various conditions
are chosen for evaluation. A large number of experiments indicate that the
proposed method significantly outperforms the previous state-of-the-art methods
in both speed and precision
Generative adversarial network for low‐light image enhancement
Low-light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade-off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low-light image enhancement. Specifically, the proposed end-to-end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi-scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low-light scenes and the realistic feature generator outperforms several state-of-the-art methods in a number of qualitative and quantitative evaluation tests