Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Zhao, Quan; Xiao, Jing; Wang, Zheng; Ma, Xujie; Wang, Mi; Satoh, Shin’ichi

doi:10.1109/tgrs.2022.3174815

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…In the on-orbit processing phase, the network model parameters obtained from ground training are used to update a lightweight deep convolutional neural network, enabling it to efficiently execute on-orbit and achieve real-time intelligent detection and processing of typical targets [12], [13]. Considering the multi-scale and multi-directional nature of remote sensing targets, the original imagery is preprocessed to focus on potential target areas.…”

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Luojia 3-01 Satellite—Real-Time Intelligent Service System for Remote Sensing Science Experiment Satellite

Wang,

Wu,

Xiao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Luojia 3-01 is the world's first intelligent remote sensing satellite, equipped with various imaging modes including video, frame-pushing, and scan-pushing. It boasts sub-meter level multi-mode optical imaging capabilities, on-orbit intelligent processing, and real-time intra-satellite and satellite-to-ground data transmission. Throughout the satellite's design and development, we have established the real-time intelligent information service architecture for Luojia 3-01, marking a new paradigm in on-orbit processing and real-time services for intelligent remote sensing satellites. We have proposed the onorbit real-time processing architecture of Luojia 3-01, addressing the challenges of limited computational and storage resources, particularly in on-orbit processing of vast volumes of remote sensing data, including core algorithmic bottlenecks in 'correction-extraction-compression'. A novel intelligent remote sensing satellite system has been developed, featuring multi-mode imaging, an open platform, intelligent processing, and satellite-toground interconnectivity, which significantly reduces the response time of remote sensing services to less than 8 minutes, thereby shortening the cycle from data acquisition to intelligent information service. This innovation spearheads a technological leap in remote sensing satellite services from data to information, from post-event to real-time, and from professional to widespread public applications, laying a solid foundation for the popularization and commercialization of China's intelligent remote sensing satellites.

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Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Luojia 3-01 Satellite—Real-Time Intelligent Service System for Remote Sensing Science Experiment Satellite

Wang,

Wu,

Xiao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Besides agricultural scenes, many achievements have been acquired in crowd counting [ 17 – 19 ], vehicle counting [ 20 ], cell counting [ 21 , 22 ], etc. Since crowd counting is the biggest and most successful branch in counting research, we mainly review the researches on crowd counting in this paper.…”

Section: Introductionmentioning

confidence: 99%

Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images

et al. 2023

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Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.

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Investigating the Resolution-Performance Trade-off of Object Detection Models in Support of the Sustainable Development Goals

Clark

Bedada

Huff

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Cited by 8 publications

References 48 publications

Luojia 3-01 Satellite—Real-Time Intelligent Service System for Remote Sensing Science Experiment Satellite

Luojia 3-01 Satellite—Real-Time Intelligent Service System for Remote Sensing Science Experiment Satellite

Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images

Investigating the Resolution-Performance Trade-off of Object Detection Models in Support of the Sustainable Development Goals

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