FRSE-Net: low-illumination object detection network based on feature representation refinement and semantic-aware enhancement-Reference-Cited by-同舟云学术

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FRSE-Net: low-illumination object detection network based on feature representation refinement and semantic-aware enhancement

Published:2023-07-21 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Jiang Zetao,Shi Daoquan^ORCID,Zhang Shaoqin

Funder

Nature Science Foundation of China

Nature Science Key Foundation of Guangxi

Guangxi Key Laboratory of Image and Graphic Intelligent Processing

Innovation Project of Guangxi Graduate Education

Innovation Project of GUET Graduate Education

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-03024-4.pdf

Reference40 articles.

1. Wei, L., Cui, W., Hu, Z., Sun, H., Hou, S.: A single-shot multi-level feature reused neural network for object detection. Vis. Comput. 37(1), 133–142 (2021)

2. Kim, S., Winovich, N., Chi, H.-G., Lin, G., Ramani, K.: Latent transformations neural network for object view synthesis. Vis. Comput. 36(8), 1663–1677 (2020)

3. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

4. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer (2014)

5. Loh, Y.P., Chan, C.S.: Getting to know low-light images with the exclusively dark dataset. Comput. Vis. Image Underst. 178, 30–42 (2019)

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3. Decouple and align classification and regression in one-stage object detection;The Visual Computer;2023-12-18

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