期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 10, 页码 18855-18863出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3161977
关键词
Feature extraction; Three-dimensional displays; Point cloud compression; Object detection; Proposals; Image segmentation; Semantics; Disparity depths; long distance; small objects; multicluster; multiscale
资金
- National Science Foundation of China [61703127, 61976074]
- National Science Foundation of Zhejiang Province of China [LY17F020026, LY21F020014]
- Major Research Plan of the National Natural Science Foundation of Zhejiang Province of China [2021C01114]
In this study, a DMF method based on disparity depths is proposed to address the low accuracy issue in transportation detection of long-distance small objects. By mapping different disparity regions to 2D candidate regions, the small-object detection problem is solved. The method clusters disparity maps of different depths and performs feature fusion of different scales. Experimental results on two datasets demonstrate that the DMF method can improve the detection accuracy of small objects.
The transportation detection of long-distance small objects has low accuracy. In this work, we propose DMF, which is based on disparity depths. We map different disparity regions to 2D candidate regions according to the distance to solve the small-object detection problem. This method clusters disparity maps of different depths. The projected image is extracted with image features in the mapping region. On the one hand, it uses a multicluster method to unsample 2D mapping regions. On the other hand, the feature fusion of different scales is performed on each cluster region. The experimental results on two datasets show that DMF can improve the detection accuracy of small objects.
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