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 ...

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 ...

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.

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.

The integration of UAVs and AI is enabling faster, safer, and more scalable ecological surveillance. Multirotor drones offer ...