VALID: A Comprehensive Virtual Aerial Image Dataset

Chen, Lyujie; Liu, Feng; Zhao, Yan; Wang, Wufan; Yuan, Xiaming; Zhu, Jihong

doi:10.1109/icra40945.2020.9197186

Cited by 26 publications

(14 citation statements)

References 31 publications

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“…Web-based data sources include car forums, search engines, and public websites. Different scenarios assess the impacted model's response to a problem [27]. The data sets' properties allow for examining underlying problems under various picture quality settings.…”

Section: Datasetmentioning

confidence: 99%

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Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

2022

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Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

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Section: Datasetmentioning

confidence: 99%

“…The fundamental issue is distortion in various image quality settings. A shot near a moving object becomes gradually distorted [27]. When a high-quality camera examines the image slowly, the form is exhibited with incredible precision and quality.…”

Section: Datasetmentioning

confidence: 99%

Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

2022

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“…Within the last few years, there has been some research that has focused on applications of synthetic data in aerial imagery. For example: Bondi et al [46] used the AirSim tool to generate BIRDSAI, an infrared object recognition dataset that blends both real and synthetic data; Chen et al [47] published VALID, a synthetic dataset for instance segmentation, semantic segmentation, and panoramic segmentation from an unmanned aircraft perspective; Kong et al [48] investigated the application of synthetic data to semantic segmentation for remote sensing and published the Synthinel-1 dataset; Shermeyer et al [49] released RarePlanes, a hybrid real-and synthetic-image dataset for aircraft detection and fine-grained attribute recognition in aerial imagery; Clement et al [50] also released a synthetic image dataset for aircraft recognition, and this is mainly intended for rotated object recognition; Lopez-Campos et al [51] released ESPADA, the first synthetic dataset for depth estimation in aerial images; Uddin et al [52] proposed a method for converting optical videos to infrared videos using GAN, investigating the impact for classification and detection task.…”

Section: Synthetic Aerial Image Datasetsmentioning

confidence: 99%

UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

Huang

et al. 2021

Remote Sensing

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As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks.

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“…Some other genre-specific datasets include scenery database [19], aerial images dataset [26,3], and street-level images dataset [18].…”

Section: Related Workmentioning

confidence: 99%

“…The era of Instagram and Flickr has seen an unprecedented overload of digital photography. According to the latest statistics shared by Omnicore Agency, 50B+ photos have been uploaded on the platform and 995 photos are uploaded every other second 2 , 350M photos are uploaded every day on Facebook 3 , and a decade is needed to view all photos on Snapchat 4 . Such an upsurge in the proliferation of data has given rise to various computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles

Singhal¹,

Liu²,

Blessing³

et al. 2021

Preprint

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The advent of social media platforms has been a catalyst for the development of digital photography that engendered a boom in vision applications. With this motivation, we introduce a large-scale dataset termed 'Photozilla', which includes over 990k images belonging to 10 different photographic styles. The dataset is then used to train 3 classification models to automatically classify the images into the relevant style which resulted in an accuracy of ∼ 96%. With the rapid evolution of digital photography, we have seen new types of photography styles emerging at an exponential rate. On that account, we present a novel Siamese-based network that uses the trained classification models as the base architecture to adapt and classify unseen styles with only 25 training samples. We report an accuracy of over 68% for identifying 10 other distinct types of photography styles. This dataset can be found at https://trisha025.github.io/Photozilla/

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VALID: A Comprehensive Virtual Aerial Image Dataset

Cited by 26 publications

References 31 publications

Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles

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