Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
November 20, 2025Scientific ReportsOpen Access

Real time road scene classification and enhancement for driver assistance under adverse weather

View Full Paper
Ask AI
Bookmark
Share

Authors

PAP P AnoopDRDeivanathan R

Discussion

Loading...

Member takes

Implication

Analysis demonstrates enhanced road visibility and scenario classification using machine learning under adverse weather, supporting driver assistance systems.

Key Points

  • Classification system achieves 98.67% accuracy using a machine learning approach, improving driver assistance in adverse weather conditions.
  • Adverse weather scenarios, including fog and rain, were effectively addressed through targeted image enhancement methods like low-light enhancement.
  • Implemented on affordable hardware, such as Raspberry Pi 5 with a USB camera, this system showcases potential for widespread application in driver assistance technologies.
  • Integration with ADAS systems allows for enhanced performance of object detection algorithms on improved road images, indicating broad future utility.

Cite This Study

Anoop et al. (2025) studied this question.

synapsesocial.com/papers/6924f084c0ce034ddc350501https://doi.org/10.1038/s41598-025-23171-z
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multi Traffic Scene Perception Based on Supervised Learning2024
  2. 2An adaptive machine learning framework for multi-scenes road surface weather condition monitoring2024 · 4 citations
  3. 3Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads2024 · 10 citations
  4. 4Development and multi-scenario validation of a real-time pavement distress monitoring system based on lightweight YOLOv82026
  5. 5Real-Time Road Condition Detection and Mapping Using YOLOv11 and Built-In Car Dashcam2026