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Cross-scale Dynamic Relation Network for Object Detection

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14325))

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Abstract

The majority of object detectors only consider the features in region proposals, without taking the global context or the relationships between objects into detection. Conceivably, it would inevitably limit the improvement of performance. To tackle the problem, we introduce a Cross-Scale Dynamic Relation Network (CSDRN) that can explore the relationships between specific objects in an image, and its core components include a Cross-Scale Semantic-Aware Module (CSSAM), Dynamic Relation Graph Reasoning (DRGR), and Semantic Attention Fusion Module(SAFM). Through the CSSAM, the crucial information in feature maps of different scales achieve semantic interaction to obtain a cross-scale semantic feature. We activate the category knowledge in the image and combine the cross-scale semantic feature to create a dynamic relationship graph. Therefore, we can get more precise relation between objects. Guided by the relation, a semantic attention is generated to enrich the visual features. Experimental results on the COCO dataset show that the proposed CSDRN can effectively improve the detection performance, reaching 54.8% box AP, which is 3.9% box AP over the baseline. Moreover, 47.6% mask AP is achieved in instance segmentation, exceeding the baseline 3.6% mask AP.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Nos. 62276073, 61966004), Guangxi Natural Science Foundation (No.2019GXNSFDA245018), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Innovation Project of Guangxi Graduate Education (Nos. YCBZ2023055, YCBZ2022060), Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

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Authors and Affiliations

  1. Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China

    Xinfang Zhong & Zhixin Li

  2. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Xinfang Zhong & Zhixin Li

Authors
  1. Xinfang Zhong
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  2. Zhixin Li
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Correspondence to Zhixin Li .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS (Malaysia), Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhong, X., Li, Z. (2024). Cross-scale Dynamic Relation Network for Object Detection. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_31

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  • DOI: https://doi.org/10.1007/978-981-99-7019-3_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7018-6

  • Online ISBN: 978-981-99-7019-3

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