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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2556-2560

Small vehicle target detection algorithm based on residual network

She Xiangyang
Han Yina
College of Computer Science & Technology, Xi'an University of Science & Technology, Xi'an 710054, China
DOI: 10.19734/j.issn.1001-3695.2019.03.0102

Abstract

Vehicle detection and identification in urban roads is of great significance for improving traffic safety and developing intelligent transportation. The traditional detection method relies on the features of manual extraction, which has been difficult to apply the complex and variable traffic scenarios, and has the defects of low recognition accuracy and high time complexity. The deep learning model can automatically extract effective features, and the generalization ability is strong, but it is difficult to classify similar vehicles more closely. To this end, this paper proposed a small vehicle target detection algorithm based on residual network. The algorithm changed the connection form of the traditional convolutional neural network to a residual connection mode based on local connection and weight sharing. At the same time, this paper changed the number of network structure control parameters, fused the features of different levels of the picture, applied the pooling layer of the region of interest to normalize the front layer features, and finally obtained the confidence and correction parameters of the target frame through the classification layer and the regression layer. Experiments show that the improved model can enhance the learning ability of the network under the premise of ensuring time efficiency, improve the average accuracy value, and obtain good detection results on the detection of similar small vehicles.

Foundation Support

陕西省自然科学基金资助项目(2017JM6105)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.03.0102
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Technology of Graphic & Image
Pages: 2556-2560
Serial Number: 1001-3695(2020)08-067-2556-05

Publish History

[2020-08-05] Printed Article

Cite This Article

厍向阳, 韩伊娜. 基于残差网络的小型车辆目标检测算法 [J]. 计算机应用研究, 2020, 37 (8): 2556-2560. (She Xiangyang, Han Yina. Small vehicle target detection algorithm based on residual network [J]. Application Research of Computers, 2020, 37 (8): 2556-2560. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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