AI is employed in object detection to identify and locate objects within images or video. It utilizes deep learning techniques, such as convolutional neural networks (CNNs), to analyze visual data, detect objects of interest, and provide bounding box coordinates, enabling applications like autonomous driving, surveillance, and image recognition.
CAGSA-YOLO, a deep learning algorithm, enhances fire safety by improving fire detection and prevention systems, achieving an mAP of 85.1% and aiding firefighters in rapid response and prevention. The algorithm integrates CARAFE upsampling, Ghost lightweight design, and SA mechanism to identify indoor fire equipment and ensure urban safety efficiently.
Researchers propose an intelligent Digital Twin framework enhanced with deep learning to detect and classify human operators and robots in human-robot collaborative manufacturing. The framework improves reliability and safety by enabling autonomous decision-making and maintaining a safe distance between humans and robots, offering a promising solution for advanced manufacturing systems.
Researchers propose a novel Transformer model with CoAttention gated vision language (CAT-ViL) embedding for surgical visual question localized answering (VQLA) tasks. The model effectively fuses multimodal features and provides localized answers, demonstrating its potential for real-world applications in surgical training and understanding.
A groundbreaking study presents a framework that leverages computer vision and artificial intelligence to automate the inspection process in the food industry, specifically for grading and sorting carrots. By incorporating RGB and depth information from a depth sensor, the system accurately identifies the geometric properties of carrots in real-time, revolutionizing traditional grading methods.
The research paper introduces DenseTextPVT, a method that uses the pyramid vision transformer (PvTv2) backbone to accurately detect dense text in scenes. It incorporates a deep multiscale feature refinement network (DMFRN) and pixel aggregation similarity vector methods to improve text detection and eliminate overlapping regions, outperforming previous methods on benchmark datasets.
Surrey University unveils a pioneering sketch-driven AI tool, promising to revolutionize object identification in images with wide applications, including transforming cancer diagnosis and aiding wildlife preservation initiatives.
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