燕山大学学报2025，Vol.49Issue(3)：247-256,264,11.DOI:10.3969/j.issn.1007-791X.2025.03.006
SFF-YOLO:空频域融合的低照度目标检测网络
SFF-YOLO:a low-light object detection network with spatial-frequency domain fusion
摘要
Abstract
To address the issues of missed detections and low accuracy in traditional object detection networks under low-light conditions,a low-light object detection network called SFF-YOLO is proposed.It's based on SFFNet and YOLOv11.First,an image enhancement network called SFFNet is introduced.It employs spatial-frequency domain fusion,where low-light images are combined with illumination guidance maps and input into a coding module for feature extraction.Then,a dual-domain fusion network,DDFNet,is designed.Image brightness is enhanced through a spatial domain processing module(SPB),and local details are repaired using a frequency domain processing module(FPB).The fused spatial-frequency domain features are concatenated with a minimum channel constraint map and fed into a decoding module for image denosing.Finally,a joint loss function is designed.End-to-end joint training of SFF-YOLO is conducted through it,which improves the model's generalization ability and object detection performance.Experiments are conducted on the LOL-v2 and ExDark datasets.The experimental results show that PSNR values of 23.11 and 25.08 as well as SSIM values of 0.851 and 0.936 are achieved by SFFNet on the LOL-v2-Real and LOL-v2-Synthetic datasets respectively,which demonstrates superior enhancement effects compared to competing networks.An accuracy of 80.4%is reached by SFF-YOLO on the ExDark dataset,which is an improvement of 3.1%over YOLOv11,with a detection speed of 91.82 frames per second,achieving high-precision real-time detection.关键词
低照度图像/目标检测/YOLOv11/空频域融合/联合训练Key words
low-light image/object detection/YOLOv11/spatial-frequency domain fusion/joint training分类
信息技术与安全科学引用本文复制引用
李扬,陈伟,朱万山,李现国,侯景忠,刘明亮..SFF-YOLO:空频域融合的低照度目标检测网络[J].燕山大学学报,2025,49(3):247-256,264,11.基金项目
天津市教委科研计划项目(2024KJ120) （2024KJ120）
