Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 997-1005· 0 citations· 13 references
Abstract
Road accidents caused by unexpected animal crossings are a major concern, especially during nighttime when visibility is poor. To address this issue, the proposed system introduces an advanced animal detection and alert framework designed to enhance road safety through continuous monitoring. The system employs a high-resolution night-vision camera to capture real-time footage of roadways. Deep learning models such as Convolutional Neural Networks (CNN) and YOLO are used to accurately identify animals even under low-light or foggy conditions. Once an animal is detected, the system immediately triggers alert signals to warn approaching vehicles, thereby reducing the chances of collision. This intelligent approach minimizes the need for human intervention and provides a scalable solution for highways and rural roads. The integration of AI-based vision technology with real-time detection ensures efficient performance and faster response. By combining automation, deep learning, and alert mechanisms, the proposed system aims to improve nighttime driving safety and prevent animal-related road accidents.
Road accidents are a major cause of injuries, fatalities, and traffic disruptions worldwide. Timely detection of vehicle accidents is critical for providing quick emergency response and reducing the impact of such incidents. This project presents an AI-powered real-time Vehicle Accident Detection system developed using...
Anisha R, K. S. Thirunavukkarasu· International Journal of Sci...· 0 citations
The proposed model addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations, and demonstrates how artificial intelligence can support modern transportation by providing faster, more accurate, and efficient accident detection while contribu...
CHENCHETI INDHU, Dr.M.Ramesh· International Journal of Eng...· 0 citations
A novel vision-based system for lane detection and roadside traffic sign recognition using advanced artificial neural network architectures that delivers fast, accurate, and robust simultaneous lane and traffic sign detection, significantly improving real-time road safety and driver assistance.
Viraj Sonawane, B. Agarkar, Sachin Chaudhari· International Journal of Adv...· 0 citations
Road traffic accidents caused by driver fatigue continue to be a major public safety concern, highlighting the need for intelligent and real-time monitoring systems. This paper presents a Smart Driver Drowsiness Detection and Advanced Emergency Communication System that combines computer vision, deep learning, and auto...
Dhanalakshmi, Venkata Yamuna Chirumalla· International Journal of Eng...· 0 citations
The proposed framework comprises sensor fusion, image processing, feature extraction, deep neural network inference and explainability mechanisms such as Gradient-weighted Class Activation Mapping, Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations.
Johan Håstad's mentor Arne Andersson, Börje Langefors· International Journal of Eme...· 0 citations
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