@inproceedings{6746553965f64a44b91e876e46219ed9,
title = "PenPy-DETR: A Penta-Pyramid Framework for Small Target Perception in Low-Light",
abstract = "Low-light object detection remains challenging due to low brightness, high noise, and small targets. Existing low-light enhancement methods often optimize for visual quality, causing feature misalignment with detection tasks. We propose Penta-Pyramid DETR (PenPy-DETR), an end-to-end framework that combines multi-scale feature enhancement with detection. It introduces two key components: a dual-pyramid enhancement network (DPE-Net) that serves as a front-end to enhance input features specifically for the detection task, and a CNN-based Enhanced Feature Fusion (CEFF) module that refines the detector's internal feature pyramid to better capture details of small targets. Experiments on the VisDrone dataset, degraded by a realistic Image Signal Processing (ISP) pipeline to simulate low-light noise and color artifacts, show that PenPy-DETR improves mAP50 from 36.3\% to 46.8\% over the RT-DETRv2 baseline, while maintaining a moderate model size of 22.9M parameters.",
keywords = "DETR, feature enhancement, low-light detection, multi-scale pyramid, small-object detection",
author = "Yanheng Wen and Yuehai Chen and Yi Shi and Jing Yang",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 IEEE Intelligent Vehicles Symposium, IV 2026 ; Conference date: 22-06-2026 Through 25-06-2026",
year = "2026",
doi = "10.1109/IV66570.2026.11624121",
language = "英语",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1362--1367",
booktitle = "2026 IEEE Intelligent Vehicles Symposium, IV 2026",
}