# PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

Source: [PE-YOLO: Pyramid Enhancement Network for Dark Object Detection](<https://www.sota2.com/research/paper/68fa70181e209131cea48f03>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:33:16.495Z

Authors: Xiangchen Yin, Zhenda Yu, Zetao Fei, Wenjun Lv, Xin Gao

Published: 2023-07-20T15:25:55.000Z

## Abstract

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://github.com/XiangchenYin/PE-YOLO.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Object Detection on ExDark](<https://www.sota2.com/research/sota/object-detection-on-exdark.md>): Object Detection; ExDark; mAP (Mean Average Precision): 78.

- [Object Detection on ExDark (test)](<https://www.sota2.com/research/sota/object-detection-on-exdark-test.md>): Object Detection; ExDark (test); mAP (Mean Average Precision): not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.10953>)

[PDF](<https://arxiv.org/pdf/2307.10953.pdf>)