CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Abstract: Deep learning models in computer vision face challenges such as high computational resource demands and limited generalization in practical scenarios. To address these issues, this study ...

Abstract: In this paper, we are exploring deep learning based image segmentation methods and evaluating the performance of different deep learning models in image segmentation tasks. U-Net, DeepLabv3+ ...

DINOv3 represents a major leap in computer vision: its frozen universal backbone and SSL approach enable researchers and developers to tackle annotation-scarce tasks, deploy high-performance models ...

Computer vision continues to be one of the most dynamic and impactful fields in artificial intelligence. Thanks to breakthroughs in deep learning, architecture design and data efficiency, machines are ...

Introduction: Accurate and automated fruit classification plays a vital role in modern agriculture but remains challenging due to the wide variability in fruit appearances. Methods: In this study, we ...

This study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial ...

Computer vision has emerged as one of the most transformative areas of artificial intelligence, with deep learning models driving unprecedented advancements in both theoretical understanding and ...

Computer vision artificial intelligence can improve patient care, but reaping its full benefits relies on careful training, data protection and validation by medical professionals. Computer vision is ...