Introduction to DETR - Part 1

DETR (Detection Transformer) is a deep learning architecture first proposed as a new approach to object detection. It’s the first object detection framework to successfully integrate transformers as a central building block in the detection pipeline.

DETR completely changes the architecture compared with previous object detection systems. In this article, we delve into the concept of Detection Transformer (DETR), a groundbreaking approach to object detection.

What is Object Detection?

According to Wikipedia, object detection is a computer technology related to computer vision and image processing that detects instances of semantic objects of a particular class (such as humans, buildings, or cars) in digital images and videos.

It’s used in self-driving cars to help the car detect lanes, other vehicles, and people walking. Object detection also helps with video surveillance and with image search. The object detection algorithms use machine learning and deep learning to detect the objects. Those are advanced ways for computers to learn independently based on looking at many sample images and videos.

How Does Object Detection Work

Object detection works by identifying and locating objects within an image or video. The process involves the following steps:

👁 Process involved in object detection

• Extracting features is the first step in object detection. This usually involves training a convolutional neural network (CNN) to recognize image patterns.
• After getting the features, the next thing is to generate object proposals - areas in the image that could contain an object. Selective search is commonly used to pump out many potential object proposals.
• The next step is to classify the object proposals as either containing an object of interest or not. This is typically done using a machine learning algorithm such as a support vector machine (SVM).
• With the proposals classified, we need to refine the bounding boxes around the objects of interest to nail their location and size. That bounding box regression adjusts the boxes to envelop the target objects.