Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Weed control is essential in apple orchards because weeds compete with trees for nutrients, water and sunlight, which can ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

Learn what CNN is in deep learning, how they work, and why they power modern image recognition AI and computer vision programs.

Researchers led by Prof. Zihan Geng at Tsinghua University and collaborators have developed a hybrid noise analysis method for single-pixel imaging in complex environments. By integrating physically ...

Scientists using a global array of radio telescopes have detected the universe’s lowest-mass dark object by observing how it warped light through gravitational lensing. The invisible mass, about a ...

Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle with ...

Introduction: Recent advances in artificial intelligence have transformed the way we analyze complex environmental data. However, high-dimensionality, spatiotemporal variability, and heterogeneous ...

Abstract: Accurate and stable target detection is crucial for robotic grasping tasks under uneven lighting conditions. To address this, this paper proposes a target object detection network (YOLO-Net) ...

Abstract: UAV imagery is widely used in areas like traffic safety, disaster rescue, and airspace management, due to its small size and low cost. However, it poses unique challenges for object ...