Abstract: Monocular 3D object detection has gained considerable attention because of its cost-effectiveness and practical applicability, particularly in autonomous driving and robotics. Most of ...

Abstract: Although existing camouflaged object detection (COD) approaches have developed various strategies to improve performance, there remains plenty of room for further improvement. The primary ...

Abstract: Object detection is a fundamental computer vision task that simultaneously locates and categorizes objects in images and videos. It is utilized in various fields, such as autonomous driving, ...

This story is the final part of CNN's As Equals series on gender inequality. For information about how the series was funded and more, check out our FAQs. The world was confronted by this form of ...

Abstract: Camouflaged Object Detection (COD) involves identifying and segmenting objects that become part of the background. It is a complicated task for Computer Vision, which requires techniques ...

ML Module — Seven classical machine learning classifiers trained on a real-world IoT sensor dataset (62,630 readings, 13 sensor channels) achieve near-perfect AUC-ROC scores above 0.999. DL Module — A ...

Abstract: Small object detection in remote sensing images remains challenging due to limited feature resolution and complex backgrounds. Conventional detectors, due to fixed receptive fields and ...

Abstract: Object Object detection, a fundamental task in computer vision, has undergone a revolutionary transformation with the advent of deep learning. This paper provides a comprehensive review of ...

Abstract: Tiny-object detection is increasingly crucial in fields such as remote sensing, traffic monitoring, and robotics. Inspired by human visual perception, the attention mechanism has become a ...

Abstract: Ensuring reliable object detection in adverse conditions is paramount for safe autonomous driving. While cameras and LiDAR struggle in such scenarios, Frequency Modulated Continuous Wave ...