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 ...

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 ...

Object detection is used in many different domains, including autonomous driving, video surveillance, and healthcare. In this post, I will briefly review the deep learning architectures that help ...

3D object detection (3DOD) is central to real-world vision systems and a critical component in the development of perception capabilities for autonomous vehicles (AVs) and mobile autonomous robots.

Object detection is used in many different domains, including autonomous driving, video surveillance, and healthcare. In this post, I will briefly review the deep learning architectures that help ...

Traditional approaches to autonomous vehicles (AVs) rely on using millions of miles of driving data in conjunction with even more miles of simulated data as inputs to supervised machine learning ...

Autonomous driving relies on processes and technologies of artificial intelligence (AI). Autonomous vehicles will soon rule our streets, weaving in and out of traffic including buses, trains, and ...

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.