Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle ...Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.展开更多
As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser （TDL） technology was presented, and the key technologies were discussed. A fie...As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser （TDL） technology was presented, and the key technologies were discussed. A field test in Guangzhou（Guangdong, China） was performed and was found that the factors, such as slope, instantaneous speed and acceleration, had significant influence on the detectable rate of the system. Based on the results, the proposal choice of testing site was presented.展开更多
以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模...以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模型。在特征提取网络中,以3×3小尺寸卷积核为基础构建带有自适应校正(Squeeze and Excitation,SE)通道注意力机制的特征提取层作为网络前三层,对小尺寸目标特征进行细粒度提取,以级联非对称卷积组构成后网络的后两层,通过更少的计算量来完成对大尺度目标的特征提取。在特征增强网络中,将所有尺度特征图融合为三层输出特征图,并利用自适应锚点框机制实现目标框定位。试验结果表明,本文提出的模型能够达到0.906的综合检测精度与31帧/秒的检测速度,并且对于多种背景下不同密集程度的汽车目标表现出良好的泛化能力。展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R136)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR28).
文摘Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning(DL)approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline model.In addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into theMFPNformultiway andmultiscale feature fusion.Finally,the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an ecosystem.The performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.
文摘As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser （TDL） technology was presented, and the key technologies were discussed. A field test in Guangzhou（Guangdong, China） was performed and was found that the factors, such as slope, instantaneous speed and acceleration, had significant influence on the detectable rate of the system. Based on the results, the proposal choice of testing site was presented.
文摘以无人机遥感影像为数据源实施多类别车辆目标的快速、精准检测,在城市道路管理及智慧城市建设等领域有重要的应用价值。针对无人机遥感影像中存在的背景复杂、车辆目标分布密集交错等问题,本文提出一种基于单阶段回归方法的车辆检测模型。在特征提取网络中,以3×3小尺寸卷积核为基础构建带有自适应校正(Squeeze and Excitation,SE)通道注意力机制的特征提取层作为网络前三层,对小尺寸目标特征进行细粒度提取,以级联非对称卷积组构成后网络的后两层,通过更少的计算量来完成对大尺度目标的特征提取。在特征增强网络中,将所有尺度特征图融合为三层输出特征图,并利用自适应锚点框机制实现目标框定位。试验结果表明,本文提出的模型能够达到0.906的综合检测精度与31帧/秒的检测速度,并且对于多种背景下不同密集程度的汽车目标表现出良好的泛化能力。
