How Object Detection works?

The general working of object detection is:

Input Image: the object detection process begins with image or video analysis.
Pre-processing: image is pre-processed to ensure suitable format for the model being used.
Feature Extraction: CNN model is used as feature extractor, the model is responsible for dissecting the image into regions and pulling out features from each region to detect patterns of different objects.
Classification: Each image region is classified into categories based on the extracted features. The classification task is performed using SVM or other neural network that computes the probability of each category present in the region.
Localization: Simultaneously with the classification process, the model determines the bounding boxes for each detected object. This involves calculating the coordinates for a box that encloses each object, thereby accurately locating it within the image.
Non-max Suppression: When the model identifies several bounding boxes for the same object, non-max suppression is used to handle these overlaps. This technique keeps only the bounding box with the highest confidence score and removes any other overlapping boxes.
Output: The process ends with the original image being marked with bounding boxes and labels that illustrate the detected objects and their corresponding categories.

What is Object Detection in Computer Vision?

Now day Object Detection is very important for Computer vision domains, this concept(Object Detection) identifies and locates objects in images or videos. Object detection finds extensive applications across various sectors. The article aims to understand the fundamentals, of working, techniques, and applications of object detection.

What is Object Detection?

In this article we are going to explore object detection with basic a , how its works and technique.

Table of Content

Understanding Object Detection
How Object Detection works?
Techniques in Object Detection

Traditional Computer Vision Techniques for Object Detection
Deep Learning Methods for Object Detection

Two-Stage Detectors for Object Detection

1. R-CNN (Regions with Convolutional Neural Networks)
2. Fast R-CNN
3. Faster R-CNN

Single-Stage Detectors for Object Detection

1. SSD (Single Shot MultiBox Detector)
2. YOLO (You Only Look Once)

Applications of Object Detection
FAQs on Object Detection

How Object Detection works?

What is Object Detection in Computer Vision?

Similar Reads

Contact Us