2026· IEEE Signal Processing Letters· Vol 33, pp. 3641-3645· 0 citations· 44 references
Abstract
Low-light object detection remains challenging because conventional image signal processing (ISP) pipelines may suppress weak sensor-level cues before detection. Existing enhancement methods usually operate on ISP-processed RGB images, making it difficult to recover subtle textures, intensity variations, and inter-channel responses degraded by denoising, tone mapping, and compression. To address this issue, this paper proposes a native four-channel Bayer RAW-domain framework that learns detection-oriented representations directly from RAW sensor data. Local RAW Signal Modulation (LRSM) enhances weak intensity and structural responses in dark regions, while Detection-Oriented RAW Signal Adaptation (DRSA) adapts multi-scale RAW features under detection-loss supervision. Experiments on RAW datasets captured with Canon, Nikon, and Sony cameras show consistent improvements over existing methods under diverse low-light conditions.
Low-light image enhancement (LLIE) is widely used as a preprocessing step for object detection under the assumption that visually enhanced images should improve downstream detection performance. However, whether this assumption holds for modern object detectors remains unclear. This paper presents a detection-oriented...
Kaung Si Thu, Kharittha Jangsamsi· International Conference on...· 0 citations
Low-light object detection is a challenging task in computer vision and visual computing, with wide applications in autonomous systems, underwater monitoring, and nighttime surveillance. Images captured under insufficient illumination usually suffer from low contrast, noise amplification, and degraded texture details,...
Object detection in low-light scenes is challenged by weak contrast, amplified noise, and spatially nonuniform exposure, which jointly degrade feature representation and increase missed detections. Existing enhancement-first pipelines can introduce additional computation or alter task-relevant appearance, while detecto...
Ze-Peng Luo, Fu-Dong Zhong, Yuan-Xiang Ni et al.· IEEE Access· 0 citations
A Frequency-domain Robust Branch is introduced to extract illumination-invariant representations from log-transformed RGB channels through frequency-domain channel differencing, zero-DC suppression, and learnable radial-basis filtering.
Ji-Shen Peng, Yu-Zhe Yang, Li-Ye Song et al.· International Conference on...· 0 citations
The experimental results show that stronger visual enhancement does not automatically improve detection and indicate that low-light enhancement for machine vision should be selected according to task-level evidence, not only according to image appearance or pixel-level metrics.
Jia-Tong Liang· International Conference on...· 0 citations
The proposed CIE-Det is an end-to-end lightweight object detection framework that jointly optimizes illumination enhancement and feature learning and achieves a favorable trade-off between detection accuracy and efficiency in low-light scenarios.
Jia-Jun Hu, Hai-Liang Wang, Min-Qin Zeng et al.· Journal of Electronic Imagin...· 0 citations
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