PE-YOLO: Pyramid Enhancement Network for Dark Object Detection
Xiangchen Yin Zhenda Yu Zetao Fei Wenjun Lv Xin Gao
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.