基于双向特征金字塔和残差网络的危化品运输车辆检测

危化品运输车辆的主要特征是车顶的危险标志和车牌下的危险品标志,这对于大多数目标检测算法来说检测起来比较困难.为了在提高检测精度的同时加快检测速度,本文提出了一种融合残差网络和双向特征金字塔网络的危化品车辆检测算法.首先通过对高速公路监控视频进行截取,制作危化品车辆数据集,然后通过残差网络进行特征提取,在本文中,使用循环...

Dangerous Chemical Transport Vehicle Detection Using Bidirectional Feature Pyramid and ResNet

The major characteristics of vehicles for hazardous chemicals transportation are the danger sign on the roof and the dangerous goods sign beside the license plate, which are difficult to detect for most object detection algorithms. To improve the detection accuracy and enhance the detection speed, this study proposes a novel detection algorithm for these vehicles based on the residual network (ResNet) and bidirectional feature pyramid network. A data set of vehicles for hazardous chemicals transportation is first made by the interception of the highway surveillance video, and then feature extraction is performed with the ResNet. In this novel model, the recurrent residual module is used to replace the middle convolution layer of the residual block. Then the bidirectional feature pyramid network is employed for feature fusion. Finally, the prediction results are obtained with the prediction network. Performance verification is carried out on the test set, and the results show that the indicators of the proposed model are superior to those of other networks overall. It has the detection accuracy up to 0.961 and the frames per second (FPS) of 43.5, showing a good industrial application prospect.