PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

TL;DR

PE-YOLO introduces a pyramid enhancement network integrated with YOLOv3 to improve dark object detection, achieving state-of-the-art results on low-light datasets through multi-resolution image decomposition and specialized enhancement modules.

Contribution

The paper presents a novel pyramid enhancement network (PENet) combined with YOLOv3 for dark object detection, featuring a detail processing module and low-frequency enhancement filter for improved performance.

Findings

Achieves 78.0% mAP on ExDark dataset.

Runs at 53.6 FPS, suitable for real-time detection.

Outperforms existing dark detectors and low-light models.

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.