Jonel R. Macalisang

Nowadays, road accidents have become a major concern. The drowsiness of drivers owing to overfatigue or tiredness, driving while intoxicated, or driving too quickly is some of the primary causes of this. Drowsy driving contributes to or increases the number of traffic accidents each year. The study presented a technique for detecting driver drowsiness in response to this issue. The sleep states of the drivers in the driving environment were detected using a deep learning approach. To assess if the eyes of particular constant face images of drivers are closed, a convolutional neural network (CNN) model has been developed. The suggested model has a wide range of possible applications, including human-computer interface design, facial expression detection, and determining driver tiredness and drowsiness. The YOLOv3 algorithm, as well as additional tools like Pascal VOC and LabelImg, were used to build this approach, which collects and trains a driver dataset that feels drowsy. The study's total detection accuracy was 100%, with detection per frame accuracy ranging from 49% to 89%.

The ocean and any form of bodies of water must be protected and secure from any intruders and to monitor our ocean, technology must be used and integrated for more efficient monitoring. Automatic boat detection plays an important role in maritime surveillance. However, the maritime environment represents lots of challenges such as the wave of water, boat movements, and weather condition. This paper presents a method for detecting moving boats from a sequence of images using a deep learning approach. In this study, the researchers proposed a detection system for the boats in the ocean using optical aerial images. The researchers conducted testing and the results were favorable. Upon testing the researchers obtained a 90% accuracy of detection of the ship in the ocean using the single images, video feeds, and live feeds. The experiments show promising results.