The kinematics and kinetics of the tilting mechanism of railway passenger cars are studied. The parameters of the mechanism are given. The motions of the actuator, the center of gravity of the carbody and the center o...The kinematics and kinetics of the tilting mechanism of railway passenger cars are studied. The parameters of the mechanism are given. The motions of the actuator, the center of gravity of the carbody and the center of coupler are calculated. Finally, the maximum driving force, output power and velocity of the actuator are discussed in detail.展开更多
Vehicle detection plays a crucial role in the field of autonomous driving technology.However,directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar perform...Vehicle detection plays a crucial role in the field of autonomous driving technology.However,directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection.Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes.This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network(FBPN).Firstly,to tackle challenges like vehicle occlusion and significant background interference,the efficient feature filtering module(EFFM)is introduced into the deep network,which amplifies the disparities between the features of the vehicle and the background.Secondly,the proposed global attention localization module(GALM)in the model neck effectively perceives the detailed position information of the target,improving both the accuracy and inference speed of themodel.Finally,the detection accuracy of small-scale vehicles is further enhanced through the utilization of a four-layer feature pyramid structure.Experimental results show that FBPN achieves an average precision of 60.8% and 97.8% on the BDD100K and KITTI datasets,respectively,with inference speeds reaching 344.83 frames/s and 357.14 frames/s.FBPN demonstrates its effectiveness and superiority by striking a balance between detection accuracy and inference speed,outperforming several state-of-the-art methods.展开更多
文摘The kinematics and kinetics of the tilting mechanism of railway passenger cars are studied. The parameters of the mechanism are given. The motions of the actuator, the center of gravity of the carbody and the center of coupler are calculated. Finally, the maximum driving force, output power and velocity of the actuator are discussed in detail.
基金funded by Ministry of Science and Technology of the People’s Republic of China,Grant Numbers 2022YFC3800502Chongqing Science and Technology Commission,Grant Number cstc2020jscx-dxwtBX0019,CSTB2022TIAD-KPX0118,cstc2020jscx-cylhX0005 and cstc2021jscx-gksbX0058.
文摘Vehicle detection plays a crucial role in the field of autonomous driving technology.However,directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection.Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes.This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network(FBPN).Firstly,to tackle challenges like vehicle occlusion and significant background interference,the efficient feature filtering module(EFFM)is introduced into the deep network,which amplifies the disparities between the features of the vehicle and the background.Secondly,the proposed global attention localization module(GALM)in the model neck effectively perceives the detailed position information of the target,improving both the accuracy and inference speed of themodel.Finally,the detection accuracy of small-scale vehicles is further enhanced through the utilization of a four-layer feature pyramid structure.Experimental results show that FBPN achieves an average precision of 60.8% and 97.8% on the BDD100K and KITTI datasets,respectively,with inference speeds reaching 344.83 frames/s and 357.14 frames/s.FBPN demonstrates its effectiveness and superiority by striking a balance between detection accuracy and inference speed,outperforming several state-of-the-art methods.
