Object Detection and Segmentation: A Hands-On Guide

Overview

Object detection and segmentation are key tasks in computer vision that go beyond simple image classification. These techniques enable AI systems to identify, locate, and outline objects within an image. Applications include autonomous vehicles, medical imaging, security surveillance, and real-time video analytics. In this project, we will implement object detection using deep learning models such as YOLO (You Only Look Once) and Faster R-CNN, along with segmentation techniques using Mask R-CNN.

Applications of Object Detection and Segmentation

Real-World Use Cases

Autonomous Vehicles: Detecting pedestrians, vehicles, and traffic signs in real-time.
Retail and Inventory Management: Identifying and counting products in warehouses and stores.
Medical Imaging: Tumor detection and segmentation in radiology images.
Security and Surveillance: Tracking and recognizing suspicious activities.
Agriculture: Identifying diseased crops and monitoring plant growth.
Augmented Reality (AR): Real-time object tracking for interactive applications.

📌 Example: An AI-powered surveillance system detects and tracks multiple objects in real-time to enhance security monitoring.

Data Collection and Preprocessing

Step 1: Choosing a Dataset

To train an object detection model, we need a labeled dataset with bounding boxes and segmentation masks. Publicly available datasets include:

COCO Dataset – A large-scale dataset for object detection and segmentation.
PASCAL VOC – A widely used dataset for object detection tasks.
Open Images Dataset – Contains diverse labeled objects for detection tasks.

📌 Task: Download the COCO dataset and inspect the annotation format.

Step 2: Data Preprocessing

Resizing Images: Standardizing image dimensions for consistent input to the model.
Data Augmentation: Applying transformations such as flipping, rotation, and contrast adjustments.
Converting Annotations: Formatting bounding boxes and segmentation masks into a compatible format for deep learning frameworks.

Code: Data Preprocessing for Object Detection

import cv2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Load and preprocess image
def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.resize(image, (416, 416))  # Resize for YOLO model
    image = image / 255.0  # Normalize pixel values
    return image

📌 Task: Implement additional augmentations such as brightness adjustment and perspective warping.

Implementing Object Detection with YOLO and Faster R-CNN

Step 1: YOLO for Real-Time Object Detection

YOLO (You Only Look Once) is a fast object detection algorithm that predicts bounding boxes and class labels in a single pass.

Code: Loading a Pretrained YOLO Model

import cv2
import numpy as np

# Load YOLO model
net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")
layers = net.getLayerNames()
out_layers = [layers[i - 1] for i in net.getUnconnectedOutLayers()]

# Process image
def detect_objects(image_path):
    image = cv2.imread(image_path)
    blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    detections = net.forward(out_layers)
    return detections

📌 Task: Modify the code to draw bounding boxes on detected objects and display class labels.

Step 2: Faster R-CNN for High-Accuracy Detection

Faster R-CNN (Region-based Convolutional Neural Network) achieves high accuracy in object detection by generating region proposals before classification.

Code: Loading a Pretrained Faster R-CNN Model in PyTorch

import torch
import torchvision.transforms as transforms
from PIL import Image
from torchvision.models.detection import fasterrcnn_resnet50_fpn

# Load Faster R-CNN model
model = fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Image preprocessing
def transform_image(image_path):
    image = Image.open(image_path).convert("RGB")
    transform = transforms.Compose([
        transforms.Resize((512, 512)),
        transforms.ToTensor()
    ])
    return transform(image).unsqueeze(0)

# Detect objects
def detect_objects(image_path):
    image = transform_image(image_path)
    outputs = model(image)
    return outputs

📌 Task: Implement non-maximum suppression (NMS) to filter overlapping bounding boxes.

Image Segmentation with Mask R-CNN

Step 1: Understanding Image Segmentation

Unlike object detection, which uses bounding boxes, segmentation assigns pixel-level masks to objects, allowing for more precise localization.

Step 2: Implementing Mask R-CNN

Mask R-CNN extends Faster R-CNN by adding a segmentation branch to generate masks for detected objects.

Code: Running Mask R-CNN for Instance Segmentation

import torchvision
from torchvision.models.detection import maskrcnn_resnet50_fpn

# Load pre-trained Mask R-CNN model
model = maskrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Process image
def segment_objects(image_path):
    image = transform_image(image_path)
    outputs = model(image)
    return outputs

📌 Task: Display segmented objects with masks overlaid on the original image.

Deploying the Object Detection Model

Step 1: Exporting the Model

Save the trained model for deployment.

torch.save(model.state_dict(), "object_detection_model.pth")

Step 2: Building an API with Flask

Deploy the trained model as a web API for real-time detection.

Code: Flask API for Object Detection

from flask import Flask, request, jsonify
import torch
from PIL import Image

app = Flask(__name__)
model = torch.load("object_detection_model.pth")
model.eval()

def preprocess_image(image_path):
    image = Image.open(image_path).convert("RGB")
    transform = transforms.Compose([
        transforms.Resize((512, 512)),
        transforms.ToTensor()
    ])
    return transform(image).unsqueeze(0)

@app.route('/detect', methods=['POST'])
def detect():
    file = request.files['file']
    image = preprocess_image(file)
    outputs = model(image)
    return jsonify(outputs)

if __name__ == '__main__':
    app.run(debug=True)

📌 Task: Modify the API to support batch image uploads and return segmented object masks.

Conclusion

Object detection and segmentation are critical for AI-driven vision applications. By leveraging YOLO, Faster R-CNN, and Mask R-CNN, we can build powerful models capable of identifying and segmenting objects in real-world environments.

✅ Key Takeaway: Combining object detection with segmentation enhances AI’s ability to interpret images beyond simple classification.

📌 Next Steps: Explore GANs for Image Generation, where AI models create synthetic images for applications such as style transfer and data augmentation.

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