Deep Learning

  We use industrial cameras to take images of steel wire ropes under different conditions, and use these wire rope images to train the U_Net network, and realize the semantic segmentation of the wire rope images by the U_Net network.

Recently, a limited number of datasets that exist are used to detect errors in the printing process of the 3D printer. Limited datasets lead most researchers to dive into sensor data fault classification.

The dataset is captured and labelled before being fed to the DL model. The image dataset is captured in a time-lapse video mode with a 15-second duration for each printing process. Next, the time-lapse is used to extract around 50 images per video. In total, 2297 images containing four classes are collected.

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.

This dataset was collected with the goal of providing researchers with access to a collection of hundreds of images for efficient classification of plant attributes and multi-instance plant localisation and detection. There are two folders, i.e. Side view and Top View.Each folder includes label files and image files in the.jpg format (.txt format). Images of 30 plants grown in 5 hydroponic systems have been collected for 66 days. Thirty plants of three species (Petunia, Pansy and Calendula) were grown in a hydroponic system for the purpose of collecting and analysing images.