Object Segmentation vs. Object Detection - Which one should you use?

object detection object segmentation Jun 29, 2022

Object Detection is one of the most popular tools in computer vision and is used in the majority of AI-CV applications. But would there be a scenario in which you would use a tool like an Object Segmentation model instead?

Well first off...

What is Object Segmentation?

To explain let's compare it to object detection in which the output is just a bounding box around the desired object. While this is ideal to solve most problems, the bounding box approach tends to have less information about the object such as the actual size and area of the object. So our goal with Object Segmentation is to detect which pixels belong to which object.

That brings us to the next question which is when would you need...

Object Segmentation over Detection?

So we already know that Segmentation provides more information than detection, right. But is it really necessary to spend additional time and effort to annotate polygons rather than bounding rectangles and which applications require such detail?

Well, there are many such applications in which you would want the pixel mask of an object such as in the case of background segmentation. A lot of YouTubers rely on green screens to segment themselves from the background because it is computationally easy. Using Object segmentation techniques like U-Net can help save you money by achieving the same result without having to buy and set up green-screen equipment which can cost anywhere between $24 to $284 on Amazon.

For medical purposes, you would want to measure the exact size of a tumor and monitor its growth over time. This can be done with object detection, but if the shape of the object is irregular then you would have an inaccurate reading.

How Can I Learn Object Segmentation?

I'm really excited to announce that I will be pre-selling my new course "U-Net Object Segmentation Pro" next week (4th July 2022) which will teach how to implement Image Segmentation using the popular U-Net Architecture.

I will be showing you how to implement Object Segmentation from scratch and how you can use it to build 8 Computer Vision Apps!

I'll be giving away ALL my development secrets and customised code notebooks that will accelerate your Object Segmentation Development. You won't find these notebooks anywhere on the internet! So that anyone with basic Python and OpenCV skills and a willingness to code, then you can replicate all the apps that we've created.

Sign up here: https://augmentedstartups.info/UnetCourseEnrollment to get a SNEAK PEEK at some of the content inside the new course and to be notified about the pre-sale!

GET A SNEAK PEEK INSIDE U-NET PRO

We currently have 50% of the tutorial for the course finished, but by the time you enroll, we plan to have just over 75% completed. :) The length of the course upon completion will be over 10 hours of tutorials and coding labs.

U-Net Image Segmentation PRO Curriculum

U-Net Object Segmentation PRO Course has 7 Modules. This covers everything including:

Module 1 | Introduction and Theory

What is Semantic Segmentation?
What is U-Net?
Effectiveness of U-Net
Architecture Comparison
Performance Comparison
Why U-Net?

Module 2 | U-Net Implementation

Going through the research paper
Encoder block implementation
Decoder block implementation
U-Net Model
- Ubuntu
- Colab

Module 3 | Dataset Creation

Dataset sources
- Where to find Standard Dataset
Creating Dataset
- Collecting Images
- Annotation
Dataset Management
- How to deal with faulty images in the dataset?
- Cleaning Datasets
Places to store the dataset(cloud options)

Module 4 | Training

The process of training U-Net
Dataset processing
Data augmentation
U-Net Model
Training
- Ubuntu
- Colab

Module 5 | Inference

Loading the model
Loading the test dataset
Predicting the mask
Evaluating the predicted mask
- F1
- mIoU
- Precision, Recall, & Accuracy
- Calculating FPS

Module 6 | Advanced Topics