无线电工程2024，Vol.54Issue(7)：1602-1613,12.DOI:10.3969/j.issn.1003-3106.2024.07.002
基于YOLOv5s微光环境下的多模态识别网络
Multimodal Recognition Network in Low Light Environment Based on YOLOv5s
摘要
Abstract
In recent years,many advanced algorithms have emerged in the fields of object detection and image segmentation.However,in low light scenarios with poor visibility,such as night and foggy weather,video images have the characteristics of high pixel noise,low contrast and no color information,which significantly limits the detection performance of the algorithm.Meanwhile,compared with mainstream RGB cameras,the millimeter wave radar has certain immunity to the complex environment mentioned above,and can assist RGB cameras in object detection under adverse conditions.Therefore,based on YOLOv5s,which has high real-time performance in single stage object detectors,and combined with the characteristics of millimeter wave radar,a multimodal recognition network is proposed for object detection in low light environments.Compared with existing sensor fusion methods,the proposed multimodal recognition network has several key advantages.Firstly,although this system integrates two modalities in a learning based manner,it only requires a small amount of labeled images and radar data for new scenes,as it can fully utilize large open source image datasets for large-scale training.This outstanding feature enables the new system to adapt to highly complex real-world environments.Secondly,due to the use of highly computationally efficient fusion methods,this system is very lightweight and therefore suitable for real-time applications in various complex scenarios.In order to evaluate the performance of this system,a small batch of radar and camera fusion dataset is produced,which includes multimodal data under normal lighting and different intensities of low light illumination.The experimental results show that the average accuracy of the multimodal recognition network in low light environments reaches 76.6％.Compared with Faster R-CNN algorithm and YOLOv7 algorithm,the mean Average Precision(mAP)has improved by 16.8％and 9.3％,and the false detection and missed detection rates are low,meeting the requirements for object recognition in low light environment.关键词
多模态识别网络/毫米波雷达/目标检测/YOLOv5s/微光场景Key words
multimodal recognition network/millimeter wave radar/object detection/YOLOv5s/low light scene分类
信息技术与安全科学引用本文复制引用
吴学礼,赵俊棋,刘雨涵,甄然..基于YOLOv5s微光环境下的多模态识别网络[J].无线电工程,2024,54(7):1602-1613,12.基金项目
国家自然科学基金(62003129) （62003129）
河北省重点研发计划项目(19250801D)National Natural Science Foundation of China(62003129) （19250801D）
Key R&D Plan Projects in Hebei Province(19250801D) （19250801D）
