Disparity-Based Multiscale Fusion Network for Transportation Detection

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 dispa...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 23; H. 10; S. 18855 - 18863
Hauptverfasser: Chen, Jing, Wang, Qichao, Peng, Weiming, Xu, Haitao, Li, Xiaodong, Xu, Wenqiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Disparity depths
Feature extraction
Image segmentation
long distance
Mapping
multicluster
multiscale
Object detection
Object recognition
Point cloud compression
Proposals
Semantics
small objects
Three-dimensional displays
Transportation
ISSN:1524-9050, 1558-0016
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3161977