基于深度学习的实时车辆检测研究

Research on Real-Time Vehicle Detection based on Deep Learning

针对城市交通复杂场景下车辆检测存在准确率低的问题,提出改进SSD(单发多箱探测器)目标检测算法。首先基于轻量化的PeleeNet(一种基于密集卷积网络的轻量化网络变体)网络结构改进SSD算法中VGG16(视觉几何群网络)特征提取网络,在保证提取丰富特征的前提下,有效地减少模型参数,提高模型的实时性;其次设计了多尺度特征融合模块和底层特征增强模块,提高特征的表达性能;最后根据数据集中目标的大小调整默认框的长宽比例,并在后三个特征层的每个单元上增加默认框。实验结果表明,改进后的目标检测算法的准确率mAP(平均精度)为79.83%,与原始SSD相比提高了2.25%,并验证了改进SSD算法的有效性。

This paper proposes an improved SSD(Single Shot Multiple Box Detector)target detection algorithm to improve the vehicle detection accuracy in complex urban traffic scenarios.Firstly,VGG16(Visual Geometry Group Network)feature extraction network algorithm in SSD is improved based on the lightweight PeleeNet(A lightweight network variant based on dense convolution network.)network structure.Under the premise of ensuring the extraction of rich features,it can effectively reduce model parameters and improve the real-time performance of the model.Secondly,a multi-scale feature fusion module and a low-level feature enhancement module are designed to improve the expression performance of features.Finally,length-width ratio of the default frame is adjusted according to the target size in data set,and the default frame is added to each cell of the last three feature layers.Experimental results show that the accuracy of mAP(mean Average Precision)of target detection algorithm is improved to 79.83%,which is 2.25%higher than the original SSD.So the improved SSD algorithm is verified to be effective.