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基于 YOLOv3 改进的交通标志识别算法

徐迎春

徐迎春. 基于 YOLOv3 改进的交通标志识别算法[J]. 数字技术与应用, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34
引用本文: 徐迎春. 基于 YOLOv3 改进的交通标志识别算法[J]. 数字技术与应用, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34
XU Ying-chun. Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34
Citation: XU Ying-chun. Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 108-111,116. doi: 10.19695/j.cnki.cn12-1369.2021.01.34

基于 YOLOv3 改进的交通标志识别算法

doi: 10.19695/j.cnki.cn12-1369.2021.01.34
基金项目: 

江苏省高校哲学社会科学研究一般项目:“风险社会”理论视域下人工智能技术风险及对策研究——基于“人 ——技术”关系分析视角(2019SJA1842)

详细信息
    作者简介:

    徐迎春(1979—),男,江苏扬州人,本科,讲师,研究方向:计算机网络技术、人工智能。

  • 中图分类号: TP391.4

Improved Traffic Sign Recognition Algorithm Based on YOLOv3 Algorithm

  • 摘要: 目标检测是计算机视觉领域较为热门的研究方向,交通标志识别在自动驾驶中有重要应用。然而复杂场景中尤其是 小目标检测精度不高。针对这一问题,提出了一种基于YOLO v3改进的算法。通过修改合适的anchor尺寸;采用Image Mixup实现 图像增广同时也增加预测框中正例样本数;改用ResNet50-D,增强特征提取能力;网络结构上输出三个尺度特征图,特征图之间通 过上采样跨尺度融合。在数据集上用改进前后的算法进行对比试验。实验结果表明,改进后的算法提高了目标检测的精度。

     

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出版历程
  • 收稿日期:  2020-11-02
  • 修回日期:  2021-01-17
  • 网络出版日期:  2021-09-23
  • 刊出日期:  2021-01-25

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