Segmentation: U-Net, Mask R-CNN • Object Detection: YOLO, SSD, Faster R-CNN • Super-Resolution CNNs
Deep Learning models have become powerful tools for understanding images.
Beyond basic CNNs, there are advanced architectures that can perform segmentation, object detection, and even improve image quality.
1. Segmentation
Segmentation means dividing an image into meaningful parts.
Instead of just saying "there is a cat", segmentation marks which pixels belong to the cat.
There are two major segmentation networks: U-Net and Mask R-CNN.
A. U-Net
U-Net is shaped like the letter "U."
It has two main parts:
Encoder
- Compresses the image
- Learns basic features such as edges and shapes
Decoder
- Expands the image
- Marks exactly where the object is
Why U-Net Is Popular
- Works very well with medical images
- Used in tasks like:
- Tumor segmentation
- Blood vessel segmentation
- Organ detection
Code Example (Simplified U-Net Block)
from tensorflow.keras import layers
def unet_block(x, filters):
x1 = layers.Conv2D(filters, 3, activation='relu', padding='same')(x)
x1 = layers.Conv2D(filters, 3, activation='relu', padding='same')(x1)
return x1B. Mask R-CNN
Mask R-CNN does two things at once:
- Detects objects (like "dog," "car," "person")
- Creates a mask showing exactly where each object is
Real Example
In a traffic camera frame:
- It detects every car
- Draws a mask on each car
- Helps self-driving systems understand the road
Why Mask R-CNN Is Useful
- Can detect multiple objects
- Works well in complex environments
2. Object Detection
Object detection means finding what objects are in an image and where they are located.
Three popular object detection architectures are YOLO, SSD, and Faster R-CNN.
A. YOLO (You Only Look Once)
YOLO is a fast, real-time object detection model.
Why YOLO is Unique
- Looks at the entire image in one go
- Extremely fast
- Used for real-time applications
Examples
- CCTV surveillance
- Self-driving cars
- Drones
Simple YOLO Inference Code
# Pseudocode: YOLO inference
results = yolo_model.predict(image)
for obj in results:
print(obj['class'], obj['confidence'])B. SSD (Single Shot Detector)
SSD also performs object detection in a single pass, like YOLO, but uses multiple feature maps at different sizes.
Strengths
- Good balance of speed and accuracy
- Works well for mobile devices
C. Faster R-CNN
Faster R-CNN is more accurate but slower.
How It Works
- First finds possible regions where objects may exist
- Then classifies each region
- Gives very high accuracy
Used In
- Medical imaging
- Satellite analysis
- High-quality security systems
3. Super-Resolution CNNs
Super-resolution means improving the quality of images by increasing their resolution using deep learning.
Why Super-Resolution Matters
- Makes blurry images sharp
- Helps restore old photos
- Useful in CCTV and forensic tools
- Improves satellite image clarity
How Super-Resolution CNNs Work
- Takes a low-resolution image
- Learns the patterns of edges, textures, and details
- Generates a high-resolution version
Famous Super-Resolution Models
- SRCNN (Super-Resolution CNN)
- ESRGAN (Enhanced Super-Resolution GAN)
Simple Code Example (SRCNN-style block)
from tensorflow.keras import layers, Sequential
model = Sequential([
layers.Conv2D(64, (9, 9), activation='relu', padding='same'),
layers.Conv2D(32, (1, 1), activation='relu', padding='same'),
layers.Conv2D(3, (5, 5), activation='linear', padding='same')
])4. Summary Table
| Task | Model | Strength |
|---|---|---|
| Segmentation | U-Net | Excellent for medical images |
| Mask R-CNN | Detects objects + masks them | |
| Object Detection | YOLO | Fast, real-time |
| SSD | Fast + mobile-friendly | |
| Faster R-CNN | Very accurate | |
| Super-Resolution | SRCNN | Simple image enhancement |
| ESRGAN | High-quality restoration |