A Complete Guide to Object Detection with Implementation in Computer Vision | Computer Vision

Object detection is a complex task in machine vision. It involves identifying and tracing objects from Pacific images or videos. Using complex deep learning architecture, analyze image data and localize objects. The output shows the name of the object and the bounding box. In this tutorial, we will describe basic object detection, some architecture for object detection, and implementation using the YOLO algorithm. So, let's dive deep into it. 

Object detection is the process of identifying and classifying particular objects from images or videos using image processing techniques.  It plays multiple roles in computer vision, such as

Safety and Security: Object detection is critical for monitoring and ensuring safety and security in various fields, such as surveillance, traffic monitoring, and food safety. 

Efficiency and Productivity: It automates tasks, increasing efficiency, productivity, and cost savings in various domains.

Enhancing Human-Machine Interaction: Object detection is essential for developing intuitive human-computer interaction systems, such as gesture recognition, facial recognition, and augmented reality applications.

Significance of Object Detection in Computer Vision:

Object detection holds immense significance in computer vision for the following reasons: 

Practical Applications: It enables computers to understand and interact with the physical world, with numerous practical applications across industries.

Efficiency and Productivity: Object detection automates tasks, leading to increased efficiency and productivity, as well as cost savings in various domains.

Safety: In applications like autonomous driving, surveillance, and industrial automation, object detection is vital for ensuring safety by detecting and responding to potential hazards.

Improved Decision-Making: It empowers systems to make informed decisions based on the objects present in the environment, leading to better outcomes in fields like robotics and healthcare.

Common Object Detection Architectures

Several deep learning architectures are used for object detection, such as R-CNN, FAST R-CNN, FASTER R-CNN, YOLO, and SSD. We will shortly describe this architecture in this tutorial.

R-CNN (Regions with CNN Features):

R-CNN is finding regions of the Pacific object in an image using a selective search. We warp all pixels in a close bounding box around the candidate region to the necessary size, regardless of its size or aspect ratio. It computes features for each proposal using a prominent CNN. approximately 2000 regions of the proposed architecture are computed CNN features.

Fast R-CNN:

Fast R-CNN is the successor algorithm of R-CNN; it's faster than R-CNN. It involves several changes to make it faster and more accurate form R-CNN. The entire image is run through a CNN to create a convolutional feature map. Using the convolutional feature map, regions of interest are found, and an ROI pooling layer is added to resize them all to the same size. Each suggestion is then forwarded to a layer that is fully connected.

Faster R-CNN:

In the previous two architectures, R-CNN and fast R-CNN use selective search algorithms. Selective search is a slow and time-consuming process. Faster R-CNN is the fact that it uses the Region Proposal Network (RPN) for generating regions of interest. Provide a bounding box on a Pacific object from the image. Improved efficiency and accuracy with less time consumption than R-CNN and fast R-CNN. Faster R-CNN is the best architecture for region-based object detection. 

  

YOLO (You Only Look Once):

YOLO (You Only Look Once) revolutionized object detection, according to Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi's paper. introducing a single-stage, real-time detection architecture. YOLO divides the input image into a grid and predicts bounding boxes and class probabilities for each grid cell simultaneously. A single neural network directly predicts bounding boxes and class probabilities from full images.  This means it only needs one pass through the network to make predictions.

SSD (Single Shot MultiBox Detector):

In 2016, Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg published a paper titled SSD: Single Shot MultiBox Detector. It's also a single-stage object detection architecture. It performs best (mean average precision) at 59 farms per second. The bounding box regression technique is multiple boxes.

Implementation part:

In our tutorial, we will implement coding par using Faster R-CNN architecture. 

Prerequisite for implementation:
Before running the implementation part, you need to know this library, OpenCV, PyTorch, TorchVison, and Matplotlib. You will get the full project code on Google Colab.

Import library:

In part, we import the required library for our object detection of images.

# Import necessary libraries
import cv2 
# OpenCV for image processing
import torch  
# PyTorch for deep learning
import torchvision.transforms as T  
# Transformations for image preprocessing
from torchvision.models.detection import fasterrcnn_resnet50_fpn  # Pre-trained Faster R-CNN model
from PIL import Image  
# Python Imaging Library for image loading

Loading Pretain model:

In this part, we load a pretrain Faster R-CNN model,

# Load a pre-trained Faster R-CNN model
model = fasterrcnn_resnet50_fpn(pretrained=True)  
# Load a pre-trained Faster R-CNN model
model.eval()  
# Set the model to evaluation mode

Load data:

This code loads an image for object detection from a specified file path. The image is opened using the PIL. We import an image that carries multiple cat objects. A transformation is applied to prepare the image for input into a PyTorch model that converts the image into a PyTorch tensor, enabling further processing.

 # Load an image for object detection
image_path = '/content/88009597_511385159797333_4560011188125040640_n.jpg'  
# Path to the image you want to detect objects in
img = Image.open(image_path)  
# Open the image using PIL

# Apply transformations to the image to prepare it for model input
transform = T.Compose([T.ToTensor()]) 
# Create a transformation that converts the image to a PyTorch tensor
img = transform(img)  
# Apply the transformation to the image
img = img.unsqueeze(0)  
# Add a batch dimension (since models expect a batch of images)

# Perform object detection
with torch.no_grad(): 
# Disable gradient calculation during inference
    predictions = model(img)  
# Forward pass through the model to get predictions

# Extract the bounding boxes, labels, and scores from the predictions
boxes = predictions[0]['boxes']  
# Extract the predicted bounding boxes
labels = predictions[0]['labels']  
# Extract the predicted class labels
scores = predictions[0]['scores']  
# Extract the confidence scores

# Set a confidence threshold to filter detections
confidence_threshold = 0.5  
# Choose a confidence threshold (adjust as needed)
filtered_indices = (scores >= confidence_threshold)  
# Find indices where scores meet the threshold

filtered_boxes = boxes[filtered_indices]  
# Filtered bounding boxes
filtered_labels = labels[filtered_indices]  
# Filtered class labels
filtered_scores = scores[filtered_indices]  
# Filtered confidence scores

# Load the image using OpenCV for visualization
image_cv2 = cv2.imread(image_path)  
# Read the image using OpenCV

# Draw bounding boxes and labels on the image
for box, label, score in zip(filtered_boxes, filtered_labels, filtered_scores):
    box = [int(val) for val in box]  
# Convert box coordinates to integers
    label_str = f"Label: {label.item()}, Score: {score:.2f}"  
# Create a label string
    cv2.rectangle(image_cv2, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)  # Draw bounding box
    cv2.putText(image_cv2, label_str, (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)  # Label

Object detection:

A pre-trained Faster R-CNN model is used in this code section to detect objects, and OpenCV is then used to visualize the items that were found. Since we are not training the model but rather producing predictions, the approach starts by utilizing the torch.no_grad() block to disable gradient computations during inference.

Display the Result with bounding box

This section of code focuses on utilizing the Matplotlib package to visualize the results of object detection. 

The following lines of code form the heart of the visualization process. First, the image is shown using plt.imshow(). But there is an important step that must come first. The image is in BGR color format because OpenCV was used to load it.

# Import the matplotlib library for displaying images
import matplotlib.pyplot as plt
# Display the result image using matplotlib
plt.imshow(cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB))  
# Convert BGR to RGB for matplotlib
plt.title("Object Detection Result") 
 # Set the title of the plot
plt.axis('off')  
# Turn off axis labels
plt.show()  
# Show the image using matplotlib

In this tutorial, we try to cover basic significance of object detection, some object detection architecture, and the implementation coding part using Faster R-CNN. It has a huge area that cannot be explained in a single tutorial. You can learn about each architecture briefly here.