Some interview questions and answers for the computer vision field. Part 6 shows some practical computer vision questions and answers.

Practical Computer Vision Interview Questions & Answers

1. Can you explain a real-world application where Object Detection is crucial?

   Answer: One practical application is in autonomous vehicles. Object Detection helps the vehicle identify and localize various objects in its surroundings, such as pedestrians, vehicles, traffic signs, and obstacles. This information is crucial for making decisions about navigation, collision avoidance, and overall safety.

2. How would you approach building a system for detecting defects in manufactured products using Computer Vision?

   Answer:

   • Data Collection: Gather a diverse dataset of images containing both defect-free and defective products.

   • Data Preprocessing: Normalize, resize, and clean the images. Annotate the images to mark the location and type of defects.

   • Model Selection: Choose a suitable architecture like a CNN. Consider transfer learning if a pre-trained model is available.

   • Training and Validation: Split the data into training and validation sets. Train the model on the training set, validate it on the validation set, and fine-tune as needed.

   • Testing and Deployment: Evaluate the model on a separate test set. Deploy it in the manufacturing environment, integrating it with the production line.

3. In a scenario where you have to detect and recognize multiple types of fruits in an image, how would you go about it?

   Answer:

   • Dataset Preparation: Gather a dataset with images of various fruits, each labeled with the corresponding fruit type.

   • Data Augmentation: Apply techniques like rotation, flipping, and scaling to increase the diversity of the dataset.

   • Model Selection: Use a CNN architecture for feature extraction. Consider approaches like YOLO or SSD for object detection.

   • Training and Evaluation: Train the model on the dataset and evaluate its performance using metrics like precision, recall, and F1-score.

   • Post-Processing: Apply Non-Maximum Suppression to remove duplicate detections.

   • Testing and Deployment: Test the model on new images, and if it performs well, deploy it for practical use.

4. How would you build a system to recognize handwritten digits in a mobile application?

   Answer:

   • Data Collection: Use a dataset like MNIST that contains images of handwritten digits (0-9).

   • Model Selection: Choose a CNN architecture, as they excel at image recognition tasks.

   • Training and Validation: Split the data into training and validation sets. Train the model and fine-tune it using backpropagation.

   • Integration with Mobile App: Use a framework like TensorFlow Lite or Core ML to convert the model to a format suitable for mobile deployment.

   • User Interface: Design a user-friendly interface for capturing or uploading images of handwritten digits within the mobile app.

   • Inference and Display: Implement code to process the image through the model and display the recognized digit.

5. How would you handle the issue of class imbalance in an Image Segmentation project?

   Answer:

   • Data Augmentation: Apply data augmentation techniques to artificially increase the number of training samples for the underrepresented class.

   • Weighted Loss Function: Assign higher weights to the loss associated with the minority class during training to give it more importance.

   • Oversampling/Undersampling: Either duplicate samples from the minority class (oversampling) or remove samples from the majority class (undersampling) to balance the classes.

   • Use of Generative Models: Techniques like Generative Adversarial Networks (GANs) can be used to generate synthetic samples for the underrepresented class.

6. What considerations should be taken into account when deploying a Computer Vision model on edge devices with limited computational resources?

   Answer:

   • Model Size: Choose a lightweight model architecture with fewer parameters to reduce memory and computation requirements.

   • Quantization: Convert the model to a lower precision format (e.g., INT8) to reduce memory and computation needs.

   • Hardware Acceleration: Utilize specialized hardware like GPUs, TPUs, or dedicated inference accelerators if available.

   • Optimization Techniques: Apply techniques like model pruning, weight sharing, and quantization-aware training to further optimize the model.

   • Model Updates: Consider strategies for remote model updates to improve performance or adapt to new conditions.