The success of deep-learning models for 2D object recognition was driven in part by the availability of large-scale high-quality datasets such as ImageNet and COCO.

Bolstering the safety of self-driving cars with a deep learning-based object detection system Date: December 13, 2022 Source: Incheon National University Summary: Self-driving cars need to ...

Apple researchers are pushing forward with efforts to bring autonomous vehicle systems to public roads, and last week published an academic paper outlining a method of detecting objects in 3D ...

Much of the innovation in image recognition relies on deep learning technology, an advanced type of machine learning and artificial intelligence. Typical machine learning takes in data, pushes it ...

(Mean average precision (mAP) is a metric for evaluating object detection in deep learning.) This achievement is hugely valuable for LiDAR perception as well as 3D mapping for autonomous driving.

3D reconstruction uses an end-to-end deep learning framework that takes a single RGB color image as input and converts the 2D image to a 3D mesh model in a more desirable camera coordinate format.

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized.