Implementation of Accident Prevention System for Hilly Regions using Deep Learning

D G Chiranth , Darshan gp , Vathsa S , Dr. Prema K N

Ghat road safety , Mountainous roadway hazards , Accident prevention system , Computer vision , Deep learning ,Object detection , YOLO-based detection, Real-time video processing , Embedded systems, Arduino Uno ,Driver alert system , Sensor fusion ,Fog and low-visibility detection ,Adaptive lighting , Wildlife detection , Roadside hazard monitoring ,Intelligent transportation systems (ITS) , Hilly region safety, Environmental sensing , Smart alert mechanisms

Ghat roads and mountainous roadways are highly accident-prone due to their complex geographical features, including blind spots, steep gradients, narrow pathways, and sharp hairpin curves. Additionally, low visibility during adverse weather conditions such as fog, heavy rainfall, or mist significantly increases the likelihood of collisions and road departures. Accidents at hairpin bends caused by limited line-of-sight and reduced visibility remain one of the major challenges for road safety authorities in these regions. The proposed system addresses these challenges by integrating image processing, deep learning-based object detection, and embedded alert mechanisms. The system deploys strategically placed high-resolution camera modules along critical curves and hazardous road segments. These cameras continuously capture live video footage of the road environment, which is processed using Python-based computer vision techniques. The captured frames are analysed using advanced object detection algorithms to identify vehicles, wildlife, and other potential obstacles in real time. This is particularly important in forest-adjacent hilly zones where animal crossings, especially by elephants, deer, or cattle, are frequent and unpredictable. The processed detection data is then communicated to an Arduino Uno microcontroller, enabling real-time driver alerting. Based on the type and proximity of the detected object, the Arduino triggers visual and audio warnings using LED indicators, buzzers, and OLED/LCD display interfaces. This immediate hazard communication enhances the driver's situational awareness, thereby reducing reaction time and preventing accidents. In addition to obstacle detection, the system incorporates environmental sensing to improve road visibility. Sensors are utilized to monitor weather conditions such as fog density and ambient light levels. Under low-visibility conditions, the system automatically activates highintensity LED lights along the roadside edges to illuminate the path and clearly mark lane boundaries. This adaptive lighting improves road perception and prevents vehicles from drifting or skidding off slopes. By combining computer vision, sensor fusion, embedded automation, and intelligent alert systems, the proposed solution creates a comprehensive accident prevention framework suitable for hilly and ghat regions. The system not only enhances road safety and driver awareness but also contributes to significantly reducing collision risks, wildlife-vehicle accidents, and weather-induced visibility hazards. Furthermore, the continuous monitoring capability supports proactive road safety management and improves the overall resilience of transportation infrastructure in challenging terrains.

Volume 13 Issue 11, November-2025

Pages : g721-g732