Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

• URL: http://arxiv.org/abs/2312.01220v2
• Date: Wed, 27 Mar 2024 17:23:16 GMT
• Title: Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
• Authors: Zhipeng Du, Miaojing Shi, Jiankang Deng,
• Abstract summary: Detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. We propose to boost low-light object detection with zero-shot day-night domain adaptation. Our method generalizes a detector from well-lit scenarios to low-light ones without requiring real low-light data.
• Score: 33.142262765252795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision, we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://github.com/ZPDu/DAI-Net.

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