[1]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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Automatic selection method of non-maximum suppression threshold based on F1 score
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 5
Page number:
1006-1012
Column:
吴文俊人工智能科学技术奖论坛
Public date:
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
- CLC:
- TP391
- DOI:
- 10.11992/tis.202006056
- 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.
- References:
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Last Update:
2021-01-15