IMAGE DETECTION USING OPENCV

A project report submitted in partial fulfillment of the requirements for the

Award of degree of Bachelor of

Technology in

rd
Computer Science and Engineering SEMESTER: 3

SESSION: 2024-2025

Submitted By

Ujjawal Rajput (System id. 2023516542)

Sunny Kumar (System id. 2023482369)

Under the Supervision of

Mr. Ajai Verma Assistant

Professor, CSE (SSET)

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

SHARDA UNIVERSITY, GREATER NOIDA

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 CERTIFICATE

I hereby declare that the work which is being presented in the project report entitled, “IMAGE DETECTION
USING OPENCV”, in partial fulfillment of the requirements for the award of degree of Bachelor of Technology
submitted in Computer Science and Engineering of Sharda University Greater Noida, is an authentic record of my
own work carried out under the supervision of Mr. Ajai Verma and refers other researcher’s works which are
duly listed in the reference section.

The matter presented in this Project has not been submitted for the award of any other degree of this or any other
university.

Ujjawal Rajput
Sunny Kumar

This is to certify that the above statement made by the candidate is correct and true to the best of my knowledge.

Mr. Ajai Verma

Assistant Professor, CSE (SSET)
Sharda University
Greater Noida

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 TABLE OF CONTENT

S.No Contents Pg. No

1 Certificate ii

2 Table of Content iii

3 List of Figures iv

4 Acknowledgement v

5 Abstract vi

6 Project Title 1

7 Team Details 2

8 Technology to be used 3
Software Platform
Hardware Platform
Tools
9 Problem Statement 4

10 Literature Survey 5

11 Project Description 7

12 Project Design & Algorithms 8

13 Implementation Methodology 9

14 Result 12

15 Conclusion 13

16 Future Scope and further enhancement of the project 14

17 Advantages of this project 15

18 Outcome 16

19` References 17

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 ACKNOWLEDGEMENT

It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during B. Tech. 2nd
Year. We owe special debt of gratitude to Mr. Ajai Verma, Department of Computer Science & Engineering,
Sharda University, Greater Noida for his constant support and guidance throughout the course of our work. His
sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only his cognizant
efforts that our endeavors have seen light of the day.
We also do not like to miss the opportunity to acknowledge the contribution of all project co-coordinators and
faculty members of the department for their kind assistance and cooperation during the development of our
project. Last but not the least, we acknowledge our friends for their contribution in the completion of the project.

Signature: Signature:
Name: Ujjawal Rajput Name: Sunny Kumar
Roll No.: 2301010919 Roll No.: 2301010879
Date: 29/11/2024 Date: 29/11/2024

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 ABSTRACT

Image detection is a fundamental application of computer vision, enabling systems to identify and localize objects
in images or video streams. This project explores the use of OpenCV, an open-source computer vision library, to
implement efficient and accurate image detection systems. OpenCV provides a wide array of tools for image
preprocessing, feature extraction, and detection, making it suitable for real-time and offline applications.The
project focuses on leveraging OpenCV’s capabilities to preprocess images, such as resizing, noise reduction, and
grayscale conversion, to enhance detection accuracy. Techniques like Haar cascades and deep learning-based
object detection models (e.g., YOLO or MobileNet) are utilized to identify objects in various scenarios.The
system is capable of detecting multiple objects, drawing bounding boxes, and annotating detections with
confidence scores, making it applicable to fields such as surveillance, healthcare, autonomous vehicles, and
augmented reality.This project emphasizes scalability and real-time processing by optimizing detection pipelines
and utilizing pre-trained models when necessary. The combination of OpenCV’s efficiency and modern detection
algorithms demonstrates the potential of image detection systems to solve complex, real-world problems with high
performance and accuracy.

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 PROJECT TITLE

The project aims to enhance image detection capabilities by integrating ChatGPT vision, leveraging advanced
machine learning techniques for image recognition, object classification, and scene understanding. Despite
significant progress in the field of computer vision, challenges such as accuracy, real-time processing, and
contextual understanding of images remain.

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 TEAM DETAILS

Our team is a group of passionate students with diverse skills in technology and design. We are dedicated to
learning and applying cutting-edge tools to solve real-world problems. Together, we bring fresh ideas and
creativity to the table, focusing on AI, web development, and user experience to build solutions that empower
people and make daily tasks easier.

1. Ujjawal Rajput (2023516542): Developer

2. Sunny Kumar (2023482369): Documentation and Designing

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 TECHNOLOGIES TO BE USED

3.1 Software Platform

 OpenCV

 Python 3.9+, TensorFlow,

 OpenCV, and pre-trained ChatGPT models

3.2 Hardware Platform

 RAM: 8GB+
 Processor: Intel i5 or higher
 OS: Windows 10/Linux

3.3 Tools

 Visual Studio Code (Editor)

 OpenCV (Library, Version 4.7)
 TensorFlow (Framework, Version 2.13)

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 PROBLEM STATEMENT

The project addresses the challenge of identifying and classifying objects in digital images. Current methods
either lack accuracy or computational efficiency. By leveraging AI and OpenCV, this project aims to create a
robust and scalable image detection system.

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 LITERATURE SURVEY

AUTHOR TITLE METHODOL FINDINGS YEAR

OGY

SMITH ET.AL OBJECT YOLO HIGH 2020

DETECTION USING ALGORITHM ACCURACY
OPEN CV IN REAL TIME

SCENARIES

Existing Techniques for Image Detection

2.1. Edge Detection

 Canny Edge Detector (1986):

o Introduced by John F. Canny, this algorithm is a popular method for edge detection, focusing on
identifying points of sharp intensity changes in an image.

2.2. Contour Detection

 Shape Detection and Analysis (1998):

o Proposed methods for contour and shape analysis in images. Contours help in identifying objects
based on their boundaries.

2.3. Template Matching

 Template Matching Algorithms (2001):

o Describes matching smaller templates within a larger image to identify object occurrences.

2.4. Haar Cascade Classifiers

 Haar-Like Features for Object Detection (Viola & Jones, 2001):

o Introduced a real-time object detection framework using Haar-like features and AdaBoost.

2.5. Deep Learning-Based Detection

 You Only Look Once (YOLO) by Redmon et al. (2016):

o Introduced a deep learning-based, real-time object detection algorithm.
o While OpenCV supports basic detection, its dnn module integrates YOLO and SSD for advanced
tasks.

3. Applications of Image Detection

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  Medical Imaging: Identifying tumors or anomalies in X-rays and MRIs (e.g., edge detection for boundary
segmentation).
 Surveillance Systems: Face and motion detection for security monitoring.
 Autonomous Vehicles: Object detection for obstacle avoidance and traffic sign recognition.
 Retail: Object tracking for inventory management and customer interaction.

4. Challenges in Image Detection

1. Lighting and Contrast Variations:

o Changes in lighting conditions may affect detection accuracy.
o Literature suggests using histogram equalization and adaptive thresholding to mitigate these
effects.
2. Occlusion and Clutter:
o Overlapping objects or noisy backgrounds can hinder accurate detection.
o Techniques like HOG (Histogram of Oriented Gradients) and CNNs (Convolutional Neural
Networks) are effective in such scenarios.
3. Real-Time Processing:
o Achieving real-time performance is crucial for applications like video surveillance.
o Haar cascades and YOLO are efficient solutions often recommended.

5. Survey of OpenCV's Capabilities

5.1. Speed and Efficiency:

OpenCV is optimized for real-time applications with C++ and Python APIs, making it a preferred choice for
image detection.

5.2. Pre-trained Models:

 Provides pre-trained Haar cascades for faces, eyes, and other objects.

5.3. Flexibility:

 OpenCV supports integration with machine learning libraries such as TensorFlow and PyTorch.

6. Recent Advances

1. Integration of Deep Learning Frameworks (2020):

o OpenCV's dnn module supports loading pre-trained models (e.g., YOLOv4, SSD).
o Advances in GPUs and cloud computing have made real-time detection feasible for large datasets.
2. OpenCV AI Kit (OAK) (2021):
o A hardware and software ecosystem enabling real-time AI-based detection with OpenCV.
3. Hybrid Techniques:
o Combining traditional methods (e.g., edge detection) with deep learning for improved accuracy.
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 PROJECT DESCRIPTION

This project involves developing a pipeline for detecting objects in images using OpenCV and deep learning.

Image detection is a critical component of computer vision, enabling systems to identify objects and their
locations within an image. This project focuses on:

 Pre-processing: Converting images to suitable formats (grayscale, resizing, etc.) for efficient processing.
 Feature Extraction: Utilizing edge detection, contours, and keypoint detection to identify features in
images.
 Object Detection: Implementing methods like Haar cascades for face detection and template matching for
object identification.
 Deep Learning Models: Optionally integrating models like YOLO or SSD for advanced and real-time
detection capabilities.
 Real-Time Detection: Enhancing the system to process live video streams for applications like
surveillance or autonomous navigation.

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 PROJECT DESIGN & ALGORITHMS

Project Design Overview:

The project is structured into several stages, starting from image acquisition to object detection and output
visualization. Below is an overview of the design:

 Input
Accepts image or video streams from:
o Pre-stored image files (.jpg, .png, etc.).
o Real-time video feeds from webcams or cameras.
o Video files (.mp4, .avi, etc.)

 Purpose: Enhance input quality and prepare data for the detection model.
 Key Steps:
o Resizing: Standardizing dimensions.
o Grayscale Conversion: Simplifying data for models like Haar cascades.
o Noise Reduction: Using filters (e.g., Gaussian blur) to remove unwanted artifacts.
o Normalization: Scaling pixel values for consistency.

 Purpose: Identify objects of interest using detection algorithms.

 Detection Options:
1. Haar Cascade Classifier: Pre-trained for specific objects like faces or eyes.
2. Deep Learning Models (via OpenCV DNN):
 YOLO (You Only Look Once) for real-time object detection.
 SSD (Single Shot Multibox Detector) for high-speed detection.
3. Feature-Based Methods (Optional):
 SIFT (Scale-Invariant Feature Transform) or ORB (Oriented FAST and Rotated BRIEF).

 Visualization:
o Draw bounding boxes around detected objects.
o Display confidence scores or labels.
 Output Modes:
o Real-time display on screen using cv2.imshow().
o Save annotated images/videos to disk.

 GPU acceleration with CUDA for faster detection.

 Frame-skipping techniques for real-time video feeds.

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 IMPLEMENTATION METHODOLOGY

Image Detection Using OpenCV

Image detection involves recognizing and locating specific objects or patterns within an image. Using OpenCV, a
popular open-source computer vision library, you can implement image detection with various algorithms. This
guide outlines the steps for building an image detection project using OpenCV.

Implementation

1. Setup

1. Install OpenCV: Ensure OpenCV is installed in your Python environment. Use the command:

bash
Copy code
pip install opencv-python opencv-python-headless

2. Import Libraries: Import necessary libraries for the project.

python
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import cv2
import numpy as np

2. Load and Preprocess the Image

 Load the image using OpenCV and convert it to grayscale (if required).
 Optionally resize the image for faster processing.

python
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# Load an image
image = cv2.imread('input.jpg')
# Convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

3. Object Detection Methods

Choose a method based on your project requirements:

1. Edge Detection (Canny): Detect edges in the image.

python
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edges = cv2.Canny(gray_image, threshold1=50, threshold2=150)
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 cv2.imshow('Edges', edges)

2. Contour Detection: Find contours to detect shapes or objects.

python
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
cv2.drawContours(image, [contour], -1, (0, 255, 0), 2)
cv2.imshow('Contours', image)

3. Template Matching: Match a smaller image (template) in a larger image.

python
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template = cv2.imread('template.jpg', 0)
result = cv2.matchTemplate(gray_image, template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
# Draw a rectangle around the detected area
h, w = template.shape
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(image, top_left, bottom_right, (255, 0, 0), 2)
cv2.imshow('Template Match', image)

4. Haar Cascades (Face/Specific Object Detection): Pre-trained models for face or object detection.

python
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow('Face Detection', image)

Methodology

Step 1: Problem Definition

 Identify the goal of the image detection (e.g., detecting faces, objects, or specific patterns).

Step 2: Dataset Collection

 Collect images for training and testing. Use pre-labeled datasets or annotate data manually using tools like
LabelImg.

Step 3: Preprocessing

 Normalize, resize, or augment images to prepare them for better detection.

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  Convert images to grayscale if color isn't essential for detection.

Step 4: Choose Detection Technique

 Use simple techniques like edge detection or contouring for basic tasks.
 Leverage pre-trained models (e.g., Haar cascades, YOLO, SSD) for advanced object detection.

Step 5: Train/Apply Models

 For custom objects, train a model using frameworks like TensorFlow or PyTorch.
 For pre-trained models, load the model and test it with your data.

Step 6: Performance Evaluation

 Evaluate using metrics like precision, recall, F1-score, and mean Average Precision (mAP).

Step 7: Optimization

 Fine-tune parameters like thresholds or model hyperparameters to improve detection accuracy.

Step 8: Deployment

 Deploy the project as a standalone application or integrate it into a web or mobile app.

Sample Project Workflow

Objective: Detect and highlight faces in an image using Haar cascades.

1. Load the image.

2. Use Haar cascades to detect faces.
3. Draw bounding boxes around detected faces.
4. Save or display the result

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 RESULT

The implementation of image detection using OpenCV and the YOLO (You Only Look Once) algorithm yielded
significant outcomes:

1. Accuracy and Speed: The YOLO-based system demonstrated real-time object detection capabilities with
high accuracy, achieving detection speeds suitable for applications like video surveillance and autonomous
vehicles.
2. Robust Detection: The model was able to detect multiple objects within a single image, even under
varying lighting conditions and partial occlusion.
3. Scalability: The solution proved scalable for diverse use cases, such as identifying specific classes of
objects in custom datasets.

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 CONCLUSION

The project successfully demonstrates the implementation of an efficient and scalable image detection system
using OpenCV. By leveraging OpenCV’s powerful tools for image preprocessing, feature extraction, and object
detection, the project achieves high accuracy and performance in identifying objects in both static images and
real-time video streams.

Key accomplishments of the project include:

1. Developing a modular pipeline for image acquisition, preprocessing, detection, and output visualization.
2. Utilizing robust detection techniques, such as Haar cascades for specific objects and deep learning-based
methods (e.g., YOLO) for generalized object detection.
3. Enhancing detection accuracy through optimized preprocessing steps, including noise reduction,
normalization, and image resizing.
4. Ensuring real-time performance through optimized implementations and optional GPU acceleration.

This project highlights the potential of image detection systems in real-world applications, including surveillance,
autonomous vehicles, healthcare diagnostics, and retail analytics. Furthermore, the flexibility of the design enables
easy adaptation for detecting a wide range of objects and scenarios.

Future improvements could focus on:

 Expanding the system to include multi-class detection capabilities.

 Integrating advanced models, such as YOLOv8 or EfficientDet, for better performance and reduced
latency.
 Incorporating custom datasets for domain-specific use cases.
 Exploring cloud-based solutions for large-scale deployment.

Overall, the project underscores the effectiveness of OpenCV as a tool for implementing computer vision
solutions and its applicability in various fields of artificial intelligence and automation.

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 FUTURE SCOPE AND FURTHER ENHANCEMENT OF THE PROJECT

The image detection system using OpenCV and YOLO presents numerous opportunities for enhancement and
broader applications. Below are the potential future scopes:

1. Integration with Edge Devices

o Deploy the system on edge devices like Raspberry Pi, NVIDIA Jetson Nano, or mobile platforms
to enable real-time detection in resource-constrained environments.
2. Advanced Object Tracking
o Extend the system to include object tracking capabilities for applications in surveillance, traffic
monitoring, and sports analytics.
3. Video Stream Processing
o Optimize the detection model for continuous video streams, enabling seamless processing of live
footage with minimal latency.
4. Custom Model Training
o Train the YOLO model on domain-specific datasets for specialized applications, such as medical
imaging, wildlife monitoring, or industrial automation.
5. Integration with Augmented Reality (AR)
o Combine detection capabilities with AR to overlay relevant information on detected objects in real
time, enhancing user experience.
6. Improved Model Efficiency
o Use model optimization techniques like pruning, quantization, or using lightweight versions (e.g.,
YOLOv5-Nano) to improve efficiency without compromising accuracy.

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 ADVANTAGES OF THIS PROJECT

The image detection system using OpenCV and YOLO offers several advantages across various domains:

1. Real-Time Performance
o The YOLO algorithm processes images quickly, making it ideal for real-time applications like
surveillance, autonomous vehicles, and live video analysis.
2. High Accuracy
o YOLO's ability to detect and classify multiple objects within a single image ensures reliable
results, even in complex environments.
3. Scalability
o The system can be easily scaled to accommodate different datasets, enabling applications in diverse
fields such as healthcare, retail, and manufacturing.
4. Flexibility
o The modularity of OpenCV and YOLO allows integration with various platforms, including
desktop, mobile, and edge devices.
5. Cost-Effectiveness
o OpenCV is an open-source library, and YOLO has lightweight models that can run on standard
hardware, reducing the need for expensive computational resources.
6. Wide Applicability
o The system can be used in applications like object tracking, face recognition, anomaly detection,
and automated quality control.

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 OUTCOME

The project outcomes highlight the successful creation of an image detection system capable of identifying objects
with precision and efficiency. Below are the key outcomes of the project:

1. Functional Image Detection System

 A fully operational pipeline that processes images or video streams to detect and annotate objects.
 Real-time object detection achieved through OpenCV’s optimized algorithms and pre-trained models.

2. Enhanced Accuracy and Performance

 Accuracy: Effective detection of objects using robust preprocessing and advanced detection techniques
like YOLO and Haar cascades.
 Efficiency: Optimized for both real-time and offline detection with provisions for GPU acceleration to
improve processing speed.

3. Versatile Applications

 Detection of objects in diverse environments, including faces, vehicles, and other general objects.
 Applicability across multiple domains, such as:
o Security and Surveillance: Real-time monitoring for safety purposes.
o Healthcare: Detecting abnormalities in medical images.
o Retail: Analyzing shopper behavior or inventory.
o Autonomous Systems: Vehicle and pedestrian detection in self-driving cars.

4. Scalable and Customizable Framework

 Flexibility to:
o Detect additional object classes by integrating new datasets and models.
o Extend functionality to include multi-class and complex object relationships.
 Scalable design suitable for both small-scale projects and large industrial applications.

5. Educational and Technical Outcomes

 Demonstrated the use of OpenCV for solving real-world computer vision problems.
 Enhanced understanding of image preprocessing, object detection algorithms, and practical challenges in
computer vision applications.

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 REFERENCES

Books and Publications

 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
 Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly
Media.

Research Papers

 Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time
Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR).

Web Resources

 OpenCV Documentation: https://docs.opencv.org

 YOLO Official GitHub Repository: https://github.com/AlexeyAB/darknet
 TensorFlow Documentation: https://www.tensorflow.org

Datasets

 COCO Dataset: https://cocodataset.org

 ImageNet: https://image-net.org

Online Tutorials and Blogs

 Adrian Rosebrock. (2020). Real-Time Object Detection with YOLO and OpenCV. PyImageSearch Blog.
 Towards Data Science. (2021). A Comprehensive Guide to YOLO for Object Detection. Medium.

Software Tools and Libraries

 OpenCV Library (Version 4.7): https://opencv.org

 TensorFlow Framework (Version 2.13): https://www.tensorflow.org

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