English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Determining the distance between the objects in a scene and the camera sensorfrom 2D images is feasible by estimating depth images using stereo cameras or3D cameras. The outcome of depth estimation is relative distances that can beused to calculate absolute distances to be applicable in reality. However,distance estimation is very challenging using 2D monocular cameras. This paperpresents a deep learning framework that consists of two deep networks for depthestimation and object detection using a single image. Firstly, objects in thescene are detected and localized using the You Only Look Once (YOLOv5) network.In parallel, the estimated depth image is computed using a deep autoencodernetwork to detect the relative distances. The proposed object detection basedYOLO was trained using a supervised learning technique, in turn, the network ofdepth estimation was self-supervised training. The presented distanceestimation framework was evaluated on real images of outdoor scenes. Theachieved results show that the proposed framework is promising and it yields anaccuracy of 96% with RMSE of 0.203 of the correct absolute distance.

Absolute distance prediction based on deep learning object detection and monocular depth estimation models