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Research on Road Multi-scale Target Detection Method Based on Improved YOLOv5s

Published: 16 May 2023 Publication History

Abstract

In the practical application of traffic target detection, it is difficult to ensure the detection accuracy of multi-scale traffic signs in real-time detection because the scale of the target varies greatly. In this paper, an improved method based on YOLOv5s is proposed to address the accuracy problem of multi-scale targets in real-time traffic detection by adapting its data enhancement at the input and localisation loss at the output respectively. The original data enhancement method is improved to solve the problem of target loss in a small range to a certain extent. In addition, an α-FEIOU algorithm is proposed to replace the original CIoU algorithm to improve the accuracy of real-time target detection. The results show that the proposed method shows good performance in different scenarios and its effectiveness is confirmed. At the same time, it has strong robustness and provides a certain reference for the application of deep learning in the field of traffic target detection.

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    cover image ACM Other conferences
    AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
    September 2022
    1221 pages
    ISBN:9781450396899
    DOI:10.1145/3573942
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 16 May 2023

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