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This paper explores the possibility of using computer vision and underwater Remotely Operated Vehicles (ROVs) to detect medical waste, such as masks and gloves in oceans. We use a single-stage detector to train the machine learning approach and then validate the results using the video feed from the tethered ROV.
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In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observab...
Citations
... On the other hand, deep learning algorithms in computer vision have recently made significant advancements. They are ideal tools to replace manual inspection processes [6][7][8][9]. The vision transformer algorithm has been trained on image data to analyze UAV images and detect potential runway defects thoroughly, such as surface water pooling and vegetation close to the runway [10]. ...
This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods.
... The You Only Look Once (YOLO) algorithm has seen extensive implementation across a range of computer vision projects, demonstrating its efficacy and precision in various applications, including forest fire assessment [12][13][14]. In particular, the YOLO-V3 detection model has been augmented through integration with the Efficient-Net method, enhancing its capabilities in fire detection [15]. ...
Forest fires have significant implications for the Earth’s ecological balance, causing widespread devastation and posing formidable challenges for containment once they propagate. The development of computer vision methods holds promise in facilitating the timely identification of forest fire risks, thereby preventing potential economic losses. In our study conducted in various regions in British Columbia, we utilized image data captured by unmanned aerial vehicles (UAVs) and computer vision methods to detect various types of trees, including alive trees, debris (logs on the ground), beetle- and fire-impacted trees, and dead trees that pose a risk of a forest fire. We then designed and implemented a novel sliding window technique to process large forest areas as georeferenced orthogonal maps. The model demonstrates proficiency in identifying various tree types, excelling in detecting healthy trees with precision and recall scores of 0.904 and 0.848, respectively. Its effectiveness in recognizing trees killed by beetles is somewhat limited, likely due to the smaller number of examples available in the dataset. After the tree types are detected, we generate color maps, indicating different fire risks to provide a new tool for fire managers to assess and implement prevention strategies. This study stands out for its integration of UAV technology and computer vision in forest fire risk assessment, marking a significant step forward in ecological protection and sustainable forest management.
... The authors [9] suggest employing CPU-based underwater ROVs to find medical waste in the ocean. They find medical waste in the ocean using an object detection algorithm, and they test their approach in trials. ...
In recent years, there have been significant advancements in Remotely Operated Vehicles (ROVs), particularly in air and underwater applications. However, the progress in land-based ROVs has been slow, necessitating innovative approaches to address the existing challenges. This research paper presents a comprehensive analysis of the current state of land-based ROV technology, focusing on its limitations. A comprehensive solution is proposed, which combines well-established technology from the gaming industry, such as steering wheel controls and accelerators, with high-performance vehicle control systems to overcome these shortcomings. The suggested approach aims to enhance the control and precision of land-based ROVs using imitation learning techniques, thereby increasing operator safety during critical missions. The paper highlights the primary features and capabilities of the proposed model while underscoring its potential to revolutionize land-based robotic systems. The researchers aspire to advance the field of remote vehicles operating in challenging terrains by providing valuable insights derived from their study. The conclusions drawn from this research significantly impact the future development and utilization of land-based ROVs, paving the way for safer and more efficient operations across various sectors and applications.
Safety is the cornerstone on which the commercial airline industry is built. However, maintaining an aircraft is expensive, and a traditional inspection takes a long time and is prone to mistakes. The time required for general visual inspections of aircraft can be drastically reduced by using deep learning and remotely piloted aircraft systems (RPAS). Deep learning techniques can be used in aircraft maintenance thanks to the availability of Graphic Processing Units. In our proof of concept study, we use YOLOv5 to build a model that uses high-quality data to find five different aircraft flaws.
