摘要
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,使用循环残差模块替换残差块的中间卷积层.接下来通过双向特征金字塔网络进行特征融合,最后通过预测网络得到预测结果.在测试集上进行性能验证,结果显示本文模型的各项指标整体上均要优于其他网络,其中检测精度达到0.961,每秒可以检测43.5张图片,整体性能表现优异,达到了检测精度和速度的均衡.
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.
作者
谢耀华
代玉
周欣
李刚
XIE Yao-Hua;DAI Yu;ZHOU Xin;LI Gang(National Engineering and Research Center for Mountainous Highways,Chongqing 400067,China;Chongqing Communications Research&Design Institute Co.Ltd.,China Merchants,Chongqing 400067,China;Research and Development Center of Transport Industry of Self-driving Technology,Chongqing 400067,China;School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China)
出处
《计算机系统应用》
2022年第1期218-225,共8页
Computer Systems & Applications
基金
国家山区公路工程技术研究中心开放基金(GSGZJ-2020-08)
广西重点研发计划(桂科AB20159032)。
关键词
双向特征金字塔
残差网络
循环残差模块
危险品车辆检测
bidirectional feature pyramid network
residual network(ResNet)
recurrent residual module
hazardous chemicals vehicle detection
