Drishya: Object Detection And MonocuLar Depth Estimation Using Deep Learning

Authors

  • Saman Tripathee Advanced College of Engineering and Management, Nepal
  • Samyam Aryal Advanced College of Engineering and Management, Nepal
  • Samiran Pratap Bibash Advanced College of Engineering and Management, Nepal

DOI:

https://doi.org/10.3126/jacem.v8i2.55951

Keywords:

accuracy, deep learning, depth detection, object detection, U-net, YOLOv5

Abstract

This project focuses on object detection and depth estimation using computer vision. The aim of this project is to develop an accurate and reliable system for detecting objects and estimating their distances from a camera. The system uses deep learning-based algorithms to detect objects and estimate their distances. The results of the project show that the system can accurately detect objects and estimate their distances in some scenarios. This project presents an Android app for real-time object detection and depth estimation using computer vision with YOLOv5 and U-Net architecture.

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Author Biographies

Saman Tripathee, Advanced College of Engineering and Management, Nepal

Department of Computer Engineering

Samyam Aryal, Advanced College of Engineering and Management, Nepal

Department of Computer Engineering

Samiran Pratap Bibash, Advanced College of Engineering and Management, Nepal

Department of Computer Engineering

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Published

2023-06-23

How to Cite

Tripathee, S., Aryal, S., & Bibash, S. P. (2023). Drishya: Object Detection And MonocuLar Depth Estimation Using Deep Learning. Journal of Advanced College of Engineering and Management, 8(2), 149–159. https://doi.org/10.3126/jacem.v8i2.55951

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Section

Articles