TL;DR
A dynamic drowsiness-assessment model based on the PERCLOS criterion is developed to quantify drowsiness through temporal analysis of eyelid-closure patterns and is developed to quantify drowsiness through temporal analysis of eyelid-closure patterns.
RiDW-YOLO, an improved detection algorithm based on YOLOv11n, provides a feasible solution for traffic-sign detection under low-light conditions and emphasizes the importance of balanced module design and frank discussion of current limitations.
A Frequency-domain Robust Branch is introduced to extract illumination-invariant representations from log-transformed RGB channels through frequency-domain channel differencing, zero-DC suppression, and learnable radial-basis filtering.
A robust visual sensing framework that integrates adaptive signal enhancement with efficient object detection and optimized YOLOv8 architecture is proposed, providing an effective solution for robust pedestrian monitoring in adverse lighting conditions, enhancing the environmental perception capabilities of intelligent...
Today’s transportation systems suffer from a high number of accidents caused by drowsy driving, so strong automated detection systems are required for implementation in the Intelligent Transportation Systems (ITS). This paper introduces an analytical framework, divided into three stages to enhance the real-time detecti...
A lightweight dual-MobileNetV2 design with platform-appropriate detectors shows promise for delivering consistent real-time drowsiness alerts across heterogeneous hardware tiers.
Driver drowsiness and over speeding are major contributors to road accidents worldwide, leading to death and serious injuries. This paper presents a real-time drowsiness and over speedingdetection. The drowsiness detection method uses a Python-based algorithm to calculate the Eye Aspect Ratio (EAR),Mouth Aspect Ratio (...
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