[1]王照国,张红云,苗夺谦.基于F1值的非极大值抑制阈值自动选取方法[J].智能系统学报,2020,15(5):1006-1012.[doi:10.11992/tis.202006056]
WANG Zhaoguo,ZHANG Hongyun,MIAO Duoqian.Automatic selection method of non-maximum suppression threshold based on F1 score[J].CAAI Transactions on Intelligent Systems,2020,15(5):1006-1012.[doi:10.11992/tis.202006056]
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WANG Zhaoguo,ZHANG Hongyun,MIAO Duoqian.Automatic selection method of non-maximum suppression threshold based on F1 score[J].CAAI Transactions on Intelligent Systems,2020,15(5):1006-1012.[doi:10.11992/tis.202006056]
基于F1值的非极大值抑制阈值自动选取方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第5期
页码:
1006-1012
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2020-09-05
- Title:
- Automatic selection method of non-maximum suppression threshold based on F1 score
- Keywords:
- computer vision; object detection; non-maximum suppression algorithm; convolutional neural network; deep learning; detection boxes; F1 value; self-adaptive algorithm
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 传统的NMS算法的过滤阈值是人为设定的，由于阈值的选取不当可能会造成漏检和误检。在应用NMS算法时，所有图像的最佳阈值不是完全相同的，根据图像自身信息的不同而发生变化。针对上述问题，提出基于F1值的非极大值抑制阈值自动选取方法，综合考虑检测算法的准确率与召回率，选取使F1值最高的最佳过滤阈值，构建映射关系。测试阶段，利用映射关系和图像信息自动选取对应的过滤阈值。实验结果表明，本文提出的改进版本NMS算法将检测精度mAP值提高了1.1%。与现有的先进算法做对比，证明了本文算法的有效性。
- Abstract:
- The filtering threshold of the traditional non-maximum suppression (NMS) algorithm is artificially set. However, the improper selection of the threshold may result in leak and error detection. When applying the NMS algorithm, the optimal threshold for all images differs because the information obtained from the image itself changes. Given the aforementioned problems, we propose an automatic selection method of the NMS threshold based on the F1 score, which comprehensively considers the accuracy and recall rates of the detection algorithm and selects the best filtering threshold based on the highest F1 score to establish a relationship map. Experimental results show that the improved version of the NMS algorithm proposed in this study enhances the detection accuracy mAP value by 1.1%. Compared with the existing advanced algorithms, the proposed algorithm has been proven to be more effective.
- 参考文献/References:
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备注/Memo
收稿日期:2020-06-30。
基金项目:国家自然科学基金项目（61573255，61976158，61673301）；国家重点研发计划项目（213）
作者简介:王照国,硕士研究生,主要研究方向为目标检测、深度学习、粒计算;张红云,副教授,博士生导师,博士,主要研究方向为主曲线算法、粒计算、模糊集。发表学术论文近50篇;苗夺谦,教授,博士生导师,博士,主要研究方向为人工智能、机器学习、大数据分析、粒计算。授权专利9项,发表学术论文180余篇,出版教材和学术著作9部
通讯作者:张红云.E-mail:zhanghongyun@tongji.edu.cn
更新日期/Last Update:
2021-01-15