Computer vision techniques for object detection

  • How to use Yolo for object detection?

    YOLO divides an input image into an S \xd7 S grid.
    If the center of an object falls into a grid cell, that grid cell is responsible for detecting that object.
    Each grid cell predicts B bounding boxes and confidence scores for those boxes..

  • Which method is best for object detection?

    Single Shot Detector (SSD) is a real-time object detection method that works by predicting the bounding boxes and class probabilities for a set of default boxes, which are pre-defined boxes of different sizes and aspect ratios.
    SSD is known for its speed and accuracy, making it a popular choice for many applications..

  • Let's look at how we can solve a general object detection problem using a CNN.

    1. First, we take an image as input:
    2. Then we divide the image into various regions:
    3. We will then consider each region as a separate image
    4. Pass all these regions (images) to the CNN and classify them into various classes
Object detection algorithms
  • Histogram of Oriented Gradients (HOG) → Introduction.
  • Region-based Convolutional Neural Networks (R-CNN) → Introduction.
  • Faster R-CNN. → Introduction.
  • Single Shot Detector (SSD) → Introduction.
  • YOLO (You Only Look Once) → Introduction.
  • RetinaNet. → Introduction.
  • ImageAI. → Introduction.
  • GluonCV.
Object detection works by using advanced computer vision techniques and deep learning algorithms to identify and locate objects within images or videos. The process involves analyzing the visual data, extracting relevant features, and using models to classify and localize the objects with bounding boxes.

What are the different types of object detection techniques?

At present, the approach of object detection has been largely evolved into two categories which are traditional machine learning methods utilizing varied computer vision techniques and deep learning method.
This article presents a review of object detection techniques.

,

Why is human detection challenging in computer vision?

Human detection is also challenging in computer vision because people as objects have an issue of various appearances and adopt a wide range of poses.
Different object detection architecture has been proposed to identify human beings from images or videos, such as:

  • pedestrian detection [ 28 ].
    • Computer vision techniques for object detection
    Computer vision techniques for object detection
    Explosive detection is a non-destructive inspection process to determine whether a container contains explosive material.
    Explosive detection is commonly used at airports, ports and for border control.

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