  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.
