QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects' information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on 'Jinghai' unmanned survey vehicles and receive excellent SOD performance from this real-world application.

Identifier

85216424031 (Scopus)

ISBN

[9798350377705]

Publication Title

IEEE International Conference on Intelligent Robots and Systems

External Full Text Location

https://doi.org/10.1109/IROS58592.2024.10801539

e-ISSN

21530866

ISSN

21530858

First Page

9581

Last Page

9587

Grant

L223019

Fund Ref

Natural Science Foundation of Beijing Municipality

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