Object Detection Under Rainy Conditions for Autonomous Vehicles

Author(s)	Ms Mythili Narra Narra
Country	India
Abstract	Object detection plays an important role in autonomous vehicle systems, especially under adverse weather conditions. Rain can significantly reduce visibility and affect the accuracy of object detection models. This project presents a web-based object detection system using YOLOv8 and OpenCV for detecting vehicles, pedestrians, and other road objects in rainy environments. The system allows users to upload rainy-condition images through a Flask-based interface and obtain detection results with bounding boxes and confidence scores. Experimental results demonstrate the effectiveness of YOLOv8 in identifying multiple objects under challenging weather conditions. The proposed system can support future autonomous driving and intelligent transportation applications.
Keywords	YOLOv8, Object Detection, Autonomous Vehicles, Rainy Conditions, Computer Vision, OpenCV, Flask, Deep Learning
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-06-28

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