针对智能车因单条引导线信息量少而引起的误识别问题,设计一种能自动识别和跟踪双边引导线的智能车系统。智能车以Freescale公司MC9S12XSl28作为核心控制器,利用COMS(Complementary Metal OxideSemiconductor)摄像头OV7620作为路径信息...针对智能车因单条引导线信息量少而引起的误识别问题,设计一种能自动识别和跟踪双边引导线的智能车系统。智能车以Freescale公司MC9S12XSl28作为核心控制器,利用COMS(Complementary Metal OxideSemiconductor)摄像头OV7620作为路径信息采集装置,对采集图像进行二值化处理、去噪操作和边缘检测后提取路径信息、进而准确地判别跑道的形状,为舵机和电机提供控制依据,以使小车平稳快速地行驶。同时,提出将行驶状态与赛道信息综合考虑的措施,并通过PID(Proportional Integral Differential)控制策略以及实验测试,实现了对各种典型跑道的优化处理,使高速行进中的智能车具有良好的转向调节能力和加减速响应能力。智能车可以在以白色为底面颜色,两边有黑色引导线的跑道上运行,克服了因单条引导线信息量少而引起的误识别问题。展开更多
Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of int...Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.展开更多
文摘针对智能车因单条引导线信息量少而引起的误识别问题,设计一种能自动识别和跟踪双边引导线的智能车系统。智能车以Freescale公司MC9S12XSl28作为核心控制器,利用COMS(Complementary Metal OxideSemiconductor)摄像头OV7620作为路径信息采集装置,对采集图像进行二值化处理、去噪操作和边缘检测后提取路径信息、进而准确地判别跑道的形状,为舵机和电机提供控制依据,以使小车平稳快速地行驶。同时,提出将行驶状态与赛道信息综合考虑的措施,并通过PID(Proportional Integral Differential)控制策略以及实验测试,实现了对各种典型跑道的优化处理,使高速行进中的智能车具有良好的转向调节能力和加减速响应能力。智能车可以在以白色为底面颜色,两边有黑色引导线的跑道上运行,克服了因单条引导线信息量少而引起的误识别问题。
基金This work was supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401+1 种基金in part,by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant Numbers SJCX21_0363in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario.It has gradually become a core technology of intelligent transportation system.Most existing vehicle re-identification models adopt the joint learning of global and local features.However,they directly use the extracted global features,resulting in insufficient feature expression.Moreover,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional costs.To solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this paper.The model consists of global and local branches.The global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation capability.In addition,multi-scale pooling operations are used to obtain multiscale information.While the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local regions.Then features from both branches are combined to provide a more comprehensive and distinctive feature representation.Extensive experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.
