Open Access

ARTICLE

Multi-Object Detection of Chinese License Plate in Complex Scenes

Dan Liu^1,3, Yajuan Wu¹, Yuxin He², Lu Qin², Bochuan Zheng^2,3,*

1 School of Computer Science, China West Normal University, Nanchong, 637002, China
2 School of Mathematics and Information, China West Normal University, Nanchong, 637002, China
3 Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong, China West Normal University, Nanchong, 637002, China

* Corresponding Author: Bochuan Zheng. Email:

Computer Systems Science and Engineering 2021, 36(1), 145-156. https://doi.org/10.32604/csse.2021.014646

Received 22 September 2020; Accepted 27 October 2020; Issue published 23 December 2020

Abstract

Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background. The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects. To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes, a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research. The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block, imbedding the SPP block into the detection network, cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task, and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes. A Chinese vehicle license plate image dataset was built for training and testing the improved network, and the location and class of the license plates in each image were accurately labeled. The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates, almost images have multiple license plates with different sizes. Experiments demonstrated that the multi-license plate detection method obtained 83.4% mAP, 98.88% precision, 98.17% recall, 98.52 F₁ score, 89.196 BFLOPS and 22 FPS on the test dataset, and whole performance was better than the other five compared networks including YOLOv3, SSD, Faster-RCNN, EfficientDet and RetinaNet.

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Keywords

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