Darknet-Based YOLO Object Detection System with Web Integration for Real- Time Image Analysis

Abstract

Object detection is a critical task in computer vision that involves identifying and
localizing objects within images or videos. With the advancement of deep learning,
object detection has become more accurate and efficient, enabling its application in
various domains such as surveillance, healthcare, autonomous driving, and smart cities.
This project presents a Darknet-based YOLO object detection system integrated with a
web-based interface for real-time image analysis.The system utilizes the YOLO (You
Only Look Once) algorithm, implemented through the Darknet framework, which is
known for its high speed and accuracy in object detection tasks. Unlike traditional
detection methods that rely on multiple stages, YOLO performs detection in a single
forward pass, making it highly efficient for real-time applications.
The proposed system allows users to upload images through a web interface built using
Django. Once an image is uploaded, it is processed using the YOLO model to detect
objects present in the image. The system identifies multiple objects, generates bounding
boxes, and assigns confidence scores to each detection. The results are then displayed
visually with annotated images, providing a clear understanding of detected objects.The
system also stores detection results, including object labels and confidence scores,
enabling users to review past detections. This feature enhances usability and supports
data analysis. Additionally, the system includes user authentication and administrative
functionalities to manage users and monitor system usage.
The Darknet framework is chosen for its optimized implementation of YOLO and its
ability to leverage GPU acceleration for faster processing. The integration with Django
ensures scalability, security, and ease of use.This project demonstrates the practical
implementation of deep learning-based object detection in a web environment