摘要
目标检测技术作为自动驾驶技术的关键环节,对于车辆实现自主导航和决策功能至关重要。现有算法在同时满足检测精度和检测速度上仍有困难。对此,文中提出一种基于RT-DETR的实时目标检测算法FSSD-DETR。该算法在主干网络中引入FADC模块,对特征提取过程进行优化;通过引入小目标检测层提升对远处车辆行人小目标的检测性能;基于SSFF模块和TFE模块对颈部网络进行了重新设计,进而提升检测的准确性;采用DySample上采样算子改善最近邻插值法可能出现的细节丢失、锯齿状边缘和图像失真等问题。实验结果表明,相比原始的RTDETR模型,改进算法在SODA10M和BDD100K数据集上mAP分别提升了3.6%和2.1%。实验说明,FSSD-DETR在保证实时性的同时显著提升了检测精度,具有应用价值。
As a key component of autonomous driving technology,object detection technology is crucial for vehicles to achieve autonomous navigation and decision-making functions.Existing algorithms still face difficulties in meeting both detection accuracy and detection speed simultaneously.In this regard,a real-time object detection algorithm FSSDDETR based on RT-DETR is proposed.This algorithm introduces FADC module into the backbone to optimize the feature extraction process.A small object detection layer is introduced to improve the detection performance of small targets for distant vehicles and pedestrians.Based on the SSFF module and TFE module,the neck network has been redesigned to improve the accuracy of detection.The DySample upsampling operator is used to replace Nearest Neighbor Interpolation to improve possible issues such as detail loss,jagged edges and image distortion.The experimental results show that compared to the original RT-DETR model,the improved algorithm has increased mAP by 3.6%and 2.1%on the SODA10M and BDD100K datasets respectively.The experiment demonstrates that FSSDDETR significantly improves the detection accuracy while ensuring real-time performance,which has application value.
作者
沈烨
王泉
吴昊
Shen Ye;Wang Quan;Wu Hao(School of Computing,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Internet of Things Engineering,Wuxi University,Wuxi 214105,China)
出处
《电子测量技术》
北大核心
2024年第14期88-95,共8页
Electronic Measurement Technology
基金
道路交通安全公安部重点实验室开放课题基金(2024ZDSYSKFKT01-2)项目资助。
