CV applications (autonomous driving, medical, retail) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a common use of computer vision in autonomous driving?

Detecting and recognizing objects like pedestrians, vehicles, and traffic signs to help the car navigate safely.

beginner

How does computer vision assist in medical imaging?

It helps identify diseases by analyzing images like X-rays or MRIs, highlighting areas of concern for doctors.

beginner

Name a computer vision application in retail.

Tracking customer movement and behavior in stores to improve layout and product placement.

intermediate

Why is real-time processing important in autonomous driving computer vision?

Because the car must quickly understand its surroundings to make safe driving decisions instantly.

intermediate

What challenge does computer vision face in medical applications?

Handling variations in image quality and differences between patients to avoid misdiagnosis.

Which of these is a key task for computer vision in autonomous driving?

A Detecting traffic signs

B Recommending movies

C Translating languages

D Managing inventory

Detecting traffic signs helps the car understand road rules and navigate safely.

In medical imaging, computer vision is mainly used to:

A Detect abnormalities in scans

B Analyze patient behavior

C Schedule appointments

D Manage hospital staff

Computer vision analyzes scans like X-rays to detect abnormalities for diagnosis.

How does computer vision help retail stores?

A By cooking food

B By delivering packages

C By cleaning shelves

D By tracking customer movement

Tracking customer movement helps stores improve layout and product placement.

Why must autonomous driving systems process images quickly?

A To save battery life

B To make safe driving decisions instantly

C To upload data to the cloud

D To entertain passengers

Quick processing is needed to react to road conditions and avoid accidents.

A challenge for medical computer vision is:

A Playing music

B Predicting stock prices

C Handling different image qualities

D Translating text

Medical images vary in quality and patient differences, which makes analysis harder.

Explain how computer vision is used in autonomous driving and why speed matters.

Think about what the car needs to see and how fast it must react.

Describe two ways computer vision helps in medical and retail fields.

Consider how images or videos are used to support doctors and store managers.

Practice

1. Which of the following is a common use of computer vision in autonomous driving?

easy

A. Detecting pedestrians and other vehicles on the road

B. Managing inventory in a warehouse

C. Analyzing blood samples in a lab

D. Recommending products to online shoppers

Solution

1. Step 1: Understand autonomous driving needs

   Autonomous cars need to see and understand their surroundings to drive safely.

2. Step 2: Match computer vision tasks to driving

   Detecting pedestrians and vehicles helps the car avoid accidents and navigate roads.

3. Final Answer:

   Detecting pedestrians and other vehicles on the road -> Option A

4. Quick Check:

   Autonomous driving = detecting road objects [OK]

Hint: Autonomous driving means seeing road and traffic [OK]

Common Mistakes:

• Confusing retail or medical uses with driving
• Thinking CV only works for product tracking
• Mixing up lab analysis with driving tasks

2. Which Python library is commonly used for image processing in computer vision tasks?

easy

A. NumPy

B. Pandas

C. OpenCV

D. Matplotlib

Solution

1. Step 1: Identify libraries for image processing

   OpenCV is designed specifically for computer vision and image tasks.

2. Step 2: Compare other libraries

   NumPy handles arrays, Pandas handles tables, Matplotlib is for plotting, but OpenCV processes images.

3. Final Answer:

   OpenCV -> Option C

4. Quick Check:

   Image processing library = OpenCV [OK]

Hint: OpenCV is the go-to for CV image tasks [OK]

Common Mistakes:

• Choosing NumPy for image processing only
• Confusing Pandas with image libraries
• Picking Matplotlib which is for plotting

3. What will the following Python code output when using a pre-trained model to classify an image in a retail store?

import cv2
model = cv2.dnn.readNetFromONNX('product_classifier.onnx')
image = cv2.imread('shelf.jpg')
blob = cv2.dnn.blobFromImage(image, 1/255.0, (224,224), swapRB=True)
model.setInput(blob)
predictions = model.forward()
print(predictions.argmax())

medium

A. The raw image pixels as a list

B. The size of the input image

C. An error because the model file is missing

D. The index of the most likely product class detected

Solution

1. Step 1: Understand the code flow

   The code loads a model, prepares the image, runs prediction, and prints the class with highest score.

2. Step 2: Interpret the output

   predictions.argmax() returns the index of the class with the highest confidence, meaning the predicted product.

3. Final Answer:

   The index of the most likely product class detected -> Option D

4. Quick Check:

   Model prediction = class index [OK]

Hint: argmax gives highest scoring class index [OK]

Common Mistakes:

• Thinking it prints raw pixels
• Assuming it prints image size
• Expecting an error without checking file presence

4. A medical imaging model is not detecting tumors correctly. The code snippet is:

image = cv2.imread('scan.png')
blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(224,224))
model.setInput(blob)
pred = model.forward()

What is the likely issue causing poor detection?

medium

A. The image is not resized before blob creation

B. The scalefactor should normalize pixel values (e.g., 1/255.0)

C. The model input is missing color channel swap

D. The model file is not loaded

Solution

1. Step 1: Check image preprocessing

   Pixel values usually need normalization (scaling to 0-1) for good model input.

2. Step 2: Identify scalefactor problem

   Using scalefactor=1.0 keeps pixel values 0-255, which can confuse the model expecting 0-1.

3. Final Answer:

   The scalefactor should normalize pixel values (e.g., 1/255.0) -> Option B

4. Quick Check:

   Normalize pixels for model input [OK]

Hint: Normalize pixels with scalefactor 1/255.0 [OK]

Common Mistakes:

• Ignoring pixel normalization
• Assuming resizing alone fixes issues
• Forgetting color channel order matters

5. In an autonomous driving system, how can computer vision help improve safety during night driving?

hard

A. By using infrared cameras to detect pedestrians in low light

B. By increasing the car's speed automatically

C. By disabling sensors to save power

D. By only relying on GPS data

Solution

1. Step 1: Understand night driving challenges

   Low light makes it hard for normal cameras to see pedestrians and obstacles.

2. Step 2: Identify CV solution for low light

   Infrared cameras capture heat signatures, helping detect people even in darkness.

3. Final Answer:

   By using infrared cameras to detect pedestrians in low light -> Option A

4. Quick Check:

   Infrared helps see in dark [OK]

Hint: Infrared cameras detect heat at night [OK]

Common Mistakes:

• Thinking speed increase improves safety
• Disabling sensors reduces safety
• Relying only on GPS ignores vision needs