基于残差网络的小型车辆目标检测算法

Small vehicle target detection algorithm based on residual network

城市道路中车辆检测与识别对于提升交通安全,发展智能化交通具有非常重要的意义。传统的检测方式依赖于人工提取的特征,已难以适用于复杂多变的交通场景,存在识别精确度低、时间复杂度高等缺陷。深度学习模型可以自动提取有用特征,泛化能力强,但难以对相似型车辆进行更加精细的分类,为此提出一种基于残差网络的小型车辆目标检测算法。算法将传统卷积神经网络的连接形式改为一种基于局部连接和权值共享的残差连接模式,同时更改网络结构控制参数数量,将图片不同层次的特征融合计算,应用感兴趣区域池化层规格化前层特征,最后经过分类层和回归层得到目标框的置信度以及修正参数。实验表明,改进模型能够在保证时间效率的前提下增强网络的学习能力,提高平均精度,在相似小型车辆的检测问题上取得了良好的检测结果。

Vehicle detection and identification in urban roads is of great significance for improving traffic safety and developing intelligent transportation.The traditional detection method relies on the features of manual extraction,which has been difficult to apply the complex and variable traffic scenarios,and has the defects of low recognition accuracy and high time complexity.The deep learning model can automatically extract effective features,and the generalization ability is strong,but it is difficult to classify similar vehicles more closely.To this end,this paper proposed a small vehicle target detection algorithm based on residual network.The algorithm changed the connection form of the traditional convolutional neural network to a residual connection mode based on local connection and weight sharing.At the same time,this paper changed the number of network structure control parameters,fused the features of different levels of the picture,applied the pooling layer of the region of interest to normalize the front layer features,and finally obtained the confidence and correction parameters of the target frame through the classification layer and the regression layer.Experiments show that the improved model can enhance the learning ability of the network under the premise of ensuring time efficiency,improve the average accuracy value,and obtain good detection results on the detection of similar small vehicles.