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 ...

Abstract: Autonomous vehicle research has grown exponentially over the years with researchers working on different object detection algorithms to realize safe and competent self-driving systems while ...

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 ...

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 ...

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 ...

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.

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.

Unsupervised learning eliminates the need for human input in creation of the AI engine. It uses unlabeled data and derives the underlying semantics and patterns which are then used to make decisions.

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