Computer Vision

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.

This folder contains folders of images.
The original folder contains the non dehazed images, default and tuned contain the dehazed counterparts.
Default folder referes to the outputs obtained using an exponent of 0.8.
Tuned refers to the images with a PSNRBR of 54 or above.
The images in paper are kept in a seperate folder.

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable.

In nighttime driving scenes, due to insufficient and uneven lighting, and the scarcity of high-quality datasets, the miss rate of nighttime pedestrian detection (PD) is much higher than that of daytime. Vision-based distance detection (DD) has the advantages of low cost and good interpretability, but the existing methods have low precision, poor robustness, and the DD is mostly performed independently of PD.