Techniques in Object Detection
Traditional Computer Vision Techniques for Object Detection
Traditionally, the task of object detection relied on manual feature extraction and classification. Some of the tradition methods are:
- Haar Cascades
- Histogram of Oriented Gradients (HOG)
- SIFT (Scale-Invariant Feature Transform)
Deep Learning Methods for Object Detection
Deep learning played an important role in revolutionizing the computer vision field. There two primary types of object detection methods:
- Two-Stage Detectors: These detectors work in two stages: first, they will propose candidate region and then classify the region into categories. Some of the two stage detectors are R-CNN, Fast R-CNN and Faster R-CNN.
- Single-stage Detectors: In a single pass, these detectors accurately forecast the bounding boxes and class probabilities for every area of the picture. YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) are two examples.
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.
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
Contact Us