An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection.

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection.

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care.

End-to-End Depth-Guided Relighting Using Lightweight Deep Learning-Based Method.

Image relighting, which involves modifying the lighting conditions while preserving the visual content, is fundamental to computer vision. This study introduced a bi-modal lightweight deep learning model for depth-guided relighting. The model utilizes the Res2Net Squeezed block's ability to capture long-range dependencies and to enhance feature representation for both the input image and its corresponding depth map.

Effect of a tropical cyclone on the distribution of heavy metals in the marine sediments off Kameswaram, Southeast coast of India.

Cyclones and heavy rainfalls are the main reasons for incessant environmental aggravation in the coastal regions and the distribution of pollutants from the contaminated terrestrial areas to the offshore regions. Twenty-five surface sediment samples were collected off Kameswaram, SE coast of India, and assessed for their geochemical and sedimentological characteristics post Cyclone Gaja. Sediment texture and various geochemical analyses were carried out to assess the metal distribution in the study area.