Some interview questions and answers for the computer vision field. Part 4 shows some object detection questions and answers.

Object Detection Interview Questions & Answers

1. What is Object Detection?

   Answer: Object Detection is a computer vision task that involves identifying and localizing objects within an image or a video stream. It differs from image classification by not only classifying the objects but also drawing bounding boxes around them.

2. What are some popular techniques for Object Detection?

   Answer: Some popular techniques for Object Detection include:

   • R-CNN (Region-based Convolutional Neural Networks)
   • Fast R-CNN
   • Faster R-CNN
   • YOLO (You Only Look Once)
   • SSD (Single Shot MultiBox Detector)
   • Mask R-CNN (for instance segmentation)

3. Explain how R-CNN works.

   Answer: R-CNN is a multi-step process:

   • It generates region proposals using a selective search algorithm.
   • Each proposal is warped to a fixed size and passed through a pre-trained CNN to extract features.
   • These features are then fed into a set of SVM classifiers to determine the presence of different object classes.
   • Finally, bounding box regression is applied to refine the locations.

4. What are the advantages of Faster R-CNN over R-CNN?

   Answer: Faster R-CNN is an improvement over R-CNN in terms of speed and efficiency. It introduces the Region Proposal Network (RPN), which shares the computations for generating region proposals with the rest of the network. This makes the process end-to-end trainable, resulting in significantly faster inference times.

5. Explain how YOLO (You Only Look Once) works.

   Answer: YOLO divides the input image into a grid and predicts bounding boxes and class probabilities directly from the grid cells. It predicts bounding box coordinates and class probabilities simultaneously using a single neural network. This makes YOLO extremely fast, as it only requires one forward pass through the network.

6. What is Non-Maximum Suppression (NMS) in Object Detection?

   Answer: Non-Maximum Suppression is a post-processing technique used to remove duplicate or overlapping bounding boxes generated by the object detection model. It keeps the bounding box with the highest confidence score while suppressing others that have significant overlap with it.

7. What is Anchor Box in Object Detection?

   Answer: Anchor boxes are a set of predefined bounding boxes with varying sizes and aspect ratios. They are used in algorithms like YOLO and SSD to predict bounding boxes of different scales and shapes for objects in the image.

8. Explain the concept of Intersection over Union (IoU) in Object Detection.

   Answer: IoU is a metric used to measure the overlap between two bounding boxes. It’s calculated by dividing the area of overlap by the area of union between the two bounding boxes. IoU is crucial for tasks like Non-Maximum Suppression.

9. What are some challenges in Object Detection?

   Answer: Challenges in Object Detection include:

   • Scale Variation: Objects may appear at different scales.
   • Occlusion: Objects may be partially or fully occluded by other objects.
   • Cluttered Background: Complex backgrounds can make object detection more challenging.
   • Class Imbalance: Some classes may be rare in the dataset, leading to imbalanced training data.

10. What are some practical applications of Object Detection?

    Answer: Object Detection has various applications including:

    • Autonomous Vehicles: Detecting pedestrians, vehicles, and traffic signs.
    • Surveillance Systems: Identifying and tracking objects or persons of interest.
    • Medical Imaging: Locating and analyzing specific features in medical images.
    • Retail: Shelf monitoring, product recognition, and inventory management.