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.

翻译：当前目标检测模型已在众多基准数据集上取得良好效果，但在暗光条件下检测目标仍是一大挑战。针对这一问题，我们提出一种金字塔增强网络（PENet），并将其与YOLOv3结合，构建了一个名为PE-YOLO的暗光目标检测框架。首先，PENet利用拉普拉斯金字塔将图像分解为四种不同分辨率的成分。具体而言，我们提出一个细节处理模块（DPM）来增强图像细节，该模块由上下文分支和边缘分支组成。此外，我们提出一个低频增强滤波器（LEF）以捕获低频语义信息并抑制高频噪声。PE-YOLO采用端到端联合训练方式，仅使用常规检测损失以简化训练流程。我们在低光照目标检测数据集ExDark上进行实验，验证了我们方法的有效性。结果表明，与其他暗光检测器和低光照增强模型相比，PE-YOLO取得了先进结果，mAP达78.0%，FPS达53.6，能够适应不同低光照条件下的目标检测任务。代码已开源至https://github.com/XiangchenYin/PE-YOLO。