Investigation of YOLO models in the detection and classification of multiple negative road anomalies

Author:

Kulkarni Swapna1,Mittal Neha1,Gupta Rajiv Ranjan2,R N Ponnlagu1

Affiliation:

1. BITS Pilani Hyderabad Campus,Department of EEE,Hyderabad,India

2. BITS Pilani Hyderabad Campus,Department of Mechanincal Engg.,Hyderabad,India

Publisher

IEEE

Reference36 articles.

1. Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions;jung;Appl Sci,2022

2. applied sciences Augmented Reality Maintenance Assistant Using YOLOv5;mendes,2021

3. Pothole Detection and Dimension Estimation System using Deep Learning (YOLO) and Image Processing

4. Vision Based Road Hump and Speed Breaker Detection;bhojane;Int Res J Eng Technol,2020

5. Cost Effective Surface Disruption Detection System for Paved and Unpaved Roads

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1. Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm;Applied Sciences;2024-06-26

2. Development of a Deep Learning-Based System for Road Traffic Anomaly Detection;2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG);2024-04-02

3. Performance Evaluation of YOLOv5s and Faster RCNN Models in Detection of Potholes from Road Surface Images;2023 IEEE 20th India Council International Conference (INDICON);2023-12-14

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