PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

• URL: http://arxiv.org/abs/2307.10953v1
• Date: Thu, 20 Jul 2023 15:25:55 GMT
• Title: PE-YOLO: Pyramid Enhancement Network for Dark Object Detection
• Authors: Xiangchen Yin, Zhenda Yu, Zetao Fei, Wenjun Lv, Xin Gao
• Abstract summary: We propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. Results: PE-YOLO achieves 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions.
• Score: 9.949687351946038
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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