Autonomous vehicles require object detection systems to navigate traffic and avoid obstacles on the road. However, current detection methods often suffer from diminished detection capabilities due ...

Autonomous electric vehicles use camera sensors for vision-based steering control and detecting both roads and objects. In this study, road and object detection are combined, utilizing the YOLOv8x-seg ...

Bolstering the safety of self-driving cars with a deep learning-based object detection system. ScienceDaily . Retrieved May 13, 2025 from www.sciencedaily.com / releases / 2022 / 12 / 221212140800.htm ...

Datasets drive vision progress, yet existing driving datasets are limited in terms of visual content, scene variation, the richness of annotations, and the geographic distribution and supported tasks ...

Abstract: In recent years, the deep learning object detection algorithms using 2D images have become the powerful tools for road object detection in autonomous driving. In fact, the deep learning ...

A self-driving vehicle has to detect objects, ... Autonomous vehicles: U of T researchers make advances with new algorithm. ... As a result, their algorithm proved up to 10 times faster when compared ...

An Israeli artificial intelligence startup gets $12.5 million in funding for a deep learning processor they plan to apply to autonomous vehicles.

Kata kunci: Object Detection, Deep Learning, YOLO (You Only Look Once), CNN (Convolutional Neural Network), Rambu Lalu Lintas. Sejauh yang diamati, belum ada pustaka dataset yang menyediakan dataset ...

Synopsis: Ritsumeikan University researchers introduce DPPFA−Net, a groundbreaking 3D object detection network melding LiDAR and image data to improve accuracy for robots and self-driving cars.

Three-dimensional object detection is crucial for autonomous vehicles. It utilizes point cloud data generated by LiDAR to ...