Density Map-based vehicle counting in remote sensing images with limited resolution

Guo, Yinong; Wu, Chen; Du, Bo; Zhang, Liangpei

doi:10.1016/j.isprsjprs.2022.05.004

Cited by 15 publications

(2 citation statements)

References 33 publications

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“…This research focuses on the unique challenges of detecting cars at large scales and different viewpoints in aerial images. By using YOLO, this study achieves good results in car detection and provides an understanding of the application of this method in a specialized context [11]. This study focuses on the use of the YOLO method in real-time detection and tracking of vehicles in images.…”

Section: Processing Datamentioning

confidence: 99%

Car Vehicle Image Object Detection Using You Only Live Once (Yolo)

Azizah,

Sahria,

Sahwari

et al. 2023

Anterior J

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This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positions. The training process will be performed using the YOLO architecture that has been pre-trained with an extensive dataset. In the testing phase, the performance of the YOLO method in car object detection will be evaluated using standard evaluation metrics such as precision, detection speed, and recall. The results of this study will show the success rate of YOLO in car detection in images and provide a better understanding of the limitations and advantages of this method. The conclusion of this research is expected to provide valuable insight into the use of the YOLO method in car object detection. This information can be used as a basis for the development and improvement of YOLO-based object detection methods, and can be applied in various applications such as automated vehicle security systems and traffic analysis.

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Section: Processing Datamentioning

confidence: 99%

Car Vehicle Image Object Detection Using You Only Live Once (Yolo)

Azizah,

Sahria,

Sahwari

et al. 2023

Anterior J

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“…UAV devices can capture photos even in remote areas where it is difficult for a human. Aerial images captured by UAV devices contain rich information [8] that can be utilized for different tasks such as traffic density estimation [9], building extraction [10], and flooded area monitoring [11].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information

Kumar

Lee

2022

Mathematics

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With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with huge variations in the appearance of objects and complex backgrounds have opened a new direction of work for researchers. The task of semantic segmentation becomes more challenging when capturing inherent features in the global and local context for UAV images. In this paper, we proposed a transformer-based encoder-decoder architecture to address this issue for the precise segmentation of UAV images. The inherent feature representation of the UAV images is exploited in the encoder network using a self-attention-based transformer framework to capture long-range global contextual information. A Token Spatial Information Fusion (TSIF) module is proposed to take advantage of a convolution mechanism that can capture local details. It fuses the local contextual details about the neighboring pixels with the encoder network and makes semantically rich feature representations. We proposed a decoder network that processes the output of the encoder network for the final semantic level prediction of each pixel. We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61.93% and 73.65%, respectively.

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Density Map Augmentation-Based Point-to-Point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution

Tan,

Guo,

2023

Lecture Notes in Computer Science

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Density Map-based vehicle counting in remote sensing images with limited resolution

Cited by 15 publications

References 33 publications

Car Vehicle Image Object Detection Using You Only Live Once (Yolo)

Car Vehicle Image Object Detection Using You Only Live Once (Yolo)

Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information

Density Map Augmentation-Based Point-to-Point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution

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