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

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community

A novel Arabidopsis pathosystem reveals cooperation of multiple hormonal response-pathways in host resistance against the global crop destroyer Macrophomina phaseolina.

Dubbed as a "global destroyer of crops", the soil-borne fungus Macrophomina phaseolina (Mp) infects more than 500 plant species including many economically important cash crops. Host defenses against infection by this pathogen are poorly understood. We established interactions between Mp and Arabidopsis thaliana (Arabidopsis) as a model system to quantitatively assess host factors affecting the outcome of Mp infections. Using agar plate-based infection assays with different Arabidopsis genotypes, we found signaling mechanisms dependent on the plant hormones ethylene, jasmonic acid and salicylic acid to control host defense against this pathogen. By profiling host transcripts in Mp-infected roots of the wild-type Arabidopsis accession Col-0 and ein2/jar1, an ethylene/jasmonic acid-signaling deficient mutant that exhibits enhanced susceptibility to this pathogen, we identified hundreds of genes potentially contributing to a diverse array of defense responses, which seem coordinated by complex interplay between multiple hormonal response-pathways. Our results establish Mp/Arabidopsis interactions as a useful model pathosystem, allowing for application of the vast genomics-related resources of this versatile model plant to the systematic investigation of previously understudied host defenses against a major crop plant pathogen