YOLOv5 is one of the latest and often used versions of a very popular deep learning neural
network used for various machine learning tasks, mainly in computer vision. The YOLO …

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train
tracks, the real-time detection of pedestrians is urgently required to ensure the safety of …

The construction site environment is quite complex with many dangerous hazards (eg,
foundation pits, holes). To avoid injuries, workers must wear helmets that are color-coded for …

Traffic control through video/image processing is a trending research topic within Intelligent
Transportation Systems. The number of moving vehicles, the density of vehicles at the …

The technology in transportation is continuously developing due to reaching the self-driving
vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It …

In 2019, the Novel Coronavirus Disease (COVID-19) was categorized as a pandemic. This
disease can be transmitted via droplets on items or surfaces within several hours. Therefore …

Images privacy involves assessing the amount of information leakage from images,
assessing risks associated with identification, and examining controls on this information. It …

In this paper, the performance of YOLOv5 and YOLOv6 models is evaluated on standardized
object classification tasks using Kaggle open-access cloud processing infrastructure. The …

Image Super-Resolution (SR) research has achieved great success with powerful neural
networks. The deeper networks with more parameters improve the restoration quality but …

There are sub-classes of pedestrians that can be defined and it is important to distinguish
between them for the detection in autonomous vehicle applications, such as elderly, and …