基于改进Deformable DETR的无人机视频流车辆目标检测算法

A vehicle object detection algorithm in UAV video stream based on improved Deformable DETR

针对无人机视频流检测中小目标数量多、因图像传输质量较低而导致的上下文语义信息不充分、传统算法融合特征推理速度慢、数据集类别样本不均衡导致的训练效果差等问题,提出一种基于改进Deformable DETR的无人机视频流车辆目标检测算法。在模型结构方面,该算法设计了跨尺度特征融合模块以增大感受野,提升小目标检测能力,并采用针对object_query的挤压-激励模块提升关键目标的响应值,减少重要目标的漏检与错检率;在数据处理方面,使用了在线困难样本挖掘技术,改善数据集中类别样本分布不均的问题。在UAVDT数据集上进行了实验,实验结果表明,改进后的算法相较于基线算法在平均检测精度上提升了1.5%,在小目标检测精度上提升了0.8%,并在保持参数量较少增长的情况下,维持了原有的检测速度。

Aiming at the problems of a large number of small targets in UAV video stream detection,insufficient contextual semantic information due to low image transmission quality,slow inference speed of traditional algorithm fusion features,and poor training effect caused by unbalanced dataset category samples,this paper proposes a vehicle object detection algorithm based on improved Deformable DETR for UAV video streaming.In terms of model structure,this method designs a cross-scale feature fusion module to increase the receptive field and improve the detection ability of small objects,and adopts the squeeze-excitation module for object_query to improve the response value of key objects and reduce the missed or false detection of important objects.In terms of data processing,online difficult sample mining technology is used to improve the problem of uneven distribution of class samples in the data set.The experimental results show that the improved algorithm improves the average detection accuracy by 1.5%and the small target detection accuracy by 1.2%compared with the baseline algorithm without detection speed degradation.