Chapter-1: Introduction
1.1 Brief Description of the Object Detection Using AI
Object detection using AI involves training algorithms to identify and locate objects
within images or videos. It employs deep learning techniques, like convolutional
neural networks, to analyze visual data and generate bounding boxes around objects
of interest. These models are trained on labeled datasets, learning to recognize
patterns and features that distinguish different objects. Object detection finds
applications in various fields, including autonomous vehicles, surveillance, retail,
and healthcare, enabling automation and efficiency in tasks like inventory
management, security monitoring, and image analysis.
1.2About the Object Detection Using AI
Object detection using AI is a computer vision task that involves training algorithms
to recognize and locate multiple objects within images or videos. Unlike image
classification, which assigns a single label to an entire image, object detection
identifies and delineates individual objects within the scene.
AI-based object detection typically employs deep learning architectures like
Convolutional Neural Networks (CNNs). These networks learn hierarchical features
from training data, enabling them to detect objects with high accuracy. Popular
object detection architectures include Faster R-CNN, YOLO (You Only Look
Once), and SSD (Single Shot Multibox Detector).
The process involves several steps:
1. Data Collection and Annotation: A large dataset of images or videos containing the
objects of interest is gathered and annotated. Annotations typically involve labeling
each object with a bounding box and its corresponding class label.
2. Model Training: The annotated dataset is used to train the object detection model.
During training, the model learns to recognize object features and predict bounding
boxes and class labels.
3. Inference: Once trained, the model can be deployed to perform inference on new,
unseen data. It analyzes input images or frames and detects objects by predicting their
bounding boxes and class labels.
4. Evaluation and Optimization: The performance of the object detection model is
evaluated using metrics such as precision, recall, and mean Average Precision (mAP).
The model may be fine-tuned or optimized to improve its accuracy and generalization
capabilities.
Object detection using AI has a wide range of applications, including:
Autonomous vehicles: Identifying pedestrians, vehicles, and other objects for
navigation and safety.
Surveillance and security: Monitoring scenes for suspicious activities or objects.
Retail: Inventory management, product recognition, and customer behavior analysis.
Healthcare: Medical imaging analysis, detecting anomalies or specific objects in scans.
Robotics: Object manipulation and interaction in robotics applications.
Overall, object detection using AI enables automation, efficiency, and enhanced
decision-making in various domains by providing machines with the ability to
perceive and understand their environment.
1.3Methodology used for Analysis, Design & Development
The Software model used is Agile model meaning of Agile is swift or versatile.
It refers to a software development approach based on iterative development.
Agile methods break tasks into smaller iterations, or parts which do not directly
involve long term planning.
The project scope and requirements are laid down at the beginning of the
development process.
Plans regarding the number of iterations, the duration and the scope of each
iteration are clearly defined in advance.
Fig 1.1 Agile Model
1.4 Methodology used for Data Collection
a) Primary Sources
i. https://towardsdatascience.com/
ii. https://paperswithcode.com/
iii. https://www.sciencedirect.com/
b) Secondary Sources
i. Get deep learning from computer vision by O’Rilley
ii. Moving Object Detection Using Machine Learning by Navneet Das
iii. Machine Learning for Image Processing: From Theory to Practice"
by Muhammad Sarfraz
1.5 System Requirement Tools
Hardware Requirements
NVIDIA/AMD Radeon CUDA-enabled GPU
Multi-core CPU or Single-core CPU
8 GB RAM
SSD Or HDD storage (500 GB or higher)
Camera
keyboard and mouse
Software Requirements
Deep learning frameworks: TensorFlow or PyTorch
CUDA-enabled GPU (NVIDIA GeForce series) with compatible drivers
for GPU acceleration
Image processing library: OpenCV
Development environment or IDE: Anaconda or Jupyter Notebook
Text editor: Visual Studio Code or Sublime Text
1.6 Project Planning
A Gantt chart is a widely used tool in project management for visualizing the
schedule of tasks and activities within a project. It provides a graphical
representation of the project timeline, showing when each task or activity is planned
to start and finish.
Fig 1.2 Gantt Chart – Synopsis
Fig 1.3 Gantt Chart – Chapter 1
Fig 1.4 Gantt Chart – Chapter 2
Fig 1.5 Gantt Chart – Chapter 3
Fig 1.6 Gantt Chart – Chapter 4
Fig 1.7 Gantt Chart – Chapter 5
Fig 1.8 Gantt Chart – Chapter 6
Fig 1.9 Gantt Chart – Final Submission
