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This dataset comprises images of parts from real industrial scenarios and virtual reality environments. Real images are sourced from actual industrial settings, ensuring both authenticity and diversity, while virtual reality images, which make up approximately 11% of the dataset, are captured through precise 3D modeling. Approximately 30% of the part information was manually authored by industry experts, while the remaining 70% was generated by multimodal large models such as Wenxin Yiyan and GPT-4.
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With multiple large open source datasets, the development of action recognition is rapid. However, we noticed the lack of annotated data of cilvil aircraft pilots, while distribution of whose action can be very different from daily casual activities. After discussion with experienced pilots and experts and close look into standard operation procedure, we present Airline-Pilot-Action (APA) benchmark, containing 5090 RGB and depth images together with corresponding flight computer data.
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This dataset consists of radiation pattern images of three distinct antennas: Patch, Monopole, and Dipole, sourced from existing literature. The database was developed using pixel sampling techniques to generate a large and diverse set of images. These images were further processed to include various geometric shapes, such as symmetric and asymmetric forms, as well as triangle and square shapes, with window sizes ranging from 2 × 2 to 100 × 100 pixels.
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81 Views
Arc faults are a significant cause of failure in photovoltaic (PV) system and can arise due to component deterioration, installation problems, rodents chewing on wires, abrasion of insulation, or other root causes. Undetected, incipient arc faults can propagate into electrical fires. Consequently, arc-fault detectors, now mandated in many jurisdictions, are essential for safe operation of PV systems.
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406 Views
The JKU-ITS AVDM contains data from 17 participants performing different tasks with various levels of distraction.
The data collection was carried out in accordance with the relevant guidelines and regulations and informed consent was obtained from all participants.
The dataset was collected using the JKU-ITS research vehicle with automated capabilities under different illumination and weather conditions along a secure test route within the
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714 Views
Various modes of transportation traverse our roadways, highlighting the importance of object classification for improving traffic safety. Optical sensors that rely on visual data encounter challenges in adverse weather conditions, where poor visibility hinders target classification. In this project we use an off-the-shelf millimeter wave Frequency Modulated Continuous Wave (FMCW) radar -- Texas Instruments IWR1843BOOST module to classify on road objects. By combining the radar module, Robot Operating System (ROS), and Python scripts, we extracted a dataset of 3D point cloud images.
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76 Views
In international contexts, natural scenes may include text in multiple languages. Especially, Latin and Arabic scene character image dataset is essential for training models to accurately detect and recognize text regions within real-world images. This is crucial for applications such as text translation, image search, content analysis, and autonomous vehicles that need to interpret text in different languages.
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354 Views
Prostate cancer is a major global health challenge. In this study, we present an approach for the detection and grading of prostate cancer through the semantic segmentation of adenocarcinoma tissues, specifically focusing on distinguishing between Gleason patterns 3 and 4. Our method leverages deep learning techniques to improve diagnostic accuracy and enhance patient treatment strategies. We developed a new dataset comprising 100 digitized whole-slide images of prostate needle core biopsy specimens, which are publicly available for research purposes.
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632 Views
In contemporary digital environments, the development of a high-resolution synthetic Latin character dataset holds paramount significance across various real-world applications within the domains of computer vision and artificial intelligence. This relevance extends from tasks such as image restoration to the implementation of sophisticated recognition systems.
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438 Views