DAFD-net: a domain adaptive feature distillation network for dark object detection based on semi-supervised learning

Guanzhi Ding; Zhenhao Yang; Xiaobin Guo

doi:10.1117/12.3035117

19 July 2024 DAFD-net: a domain adaptive feature distillation network for dark object detection based on semi-supervised learning

Guanzhi Ding, Zhenhao Yang, Xiaobin Guo

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321324 (2024) https://doi.org/10.1117/12.3035117
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Object detection represents a fundamental task within the realm of computer vision. However, achieving object detection in the dark is still a substantial challenge due to the low contrast of images and the lack of large-scale labeled dark image datasets. One possible solution is to transfer the knowledge of the trained model from a normal illumination source domain to a dark target domain by developing a domain adaptation method. Therefore, we propose a domain adaptive feature distillation network (DAFD-Net) to improve the accuracy of dark object detection. Specifically, we guide the feature extraction of source and target domains by adopting the knowledge distillation training strategy of the teacher-student model and introduce domain adversarial loss and feature distillation loss to jointly conduct semi-supervised optimal learning of the network. Experimental results demonstrate that our DAFD-Net achieves superior performance on the semi- supervised dark detection task.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guanzhi Ding, Zhenhao Yang, and Xiaobin Guo "DAFD-net: a domain adaptive feature distillation network for dark object detection based on semi-supervised learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321324 (19 July 2024); https://doi.org/10.1117/12.3035117

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KEYWORDS

Object detection

Light sources and illumination

Network architectures

Target detection

Performance modeling

Autoregressive models

Ablation

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