DETR (Detection Transformer) is a revolutionary deep learning architecture initially proposed as a novel approach to object detection. It stands out as the first object detection framework to successfully incorporate transformers as a core building block in the detection pipeline.
DETR brings about a significant change in architecture compared to previous object detection systems. This article explores the concept of Detection Transformer (DETR), presenting a groundbreaking approach to object detection.
What is Object Detection?
Object detection, according to Wikipedia, is a computer technology within computer vision and image processing that identifies instances of semantic objects of a specific class (such as humans, buildings, or cars) in digital images and videos.
It is widely utilized in applications like self-driving cars for lane detection, vehicle detection, and pedestrian detection. Object detection also plays a crucial role in video surveillance and image search. Machine learning and deep learning algorithms are employed in object detection to identify objects by learning from a vast number of sample images and videos.
How Does Object Detection Work
Object detection involves identifying and locating objects within an image or video. The process typically includes the following steps:
- Feature Extraction: The initial step in object detection involves extracting features, often achieved by training a convolutional neural network (CNN) to recognize patterns in images.
- Object Proposal Generation: Following feature extraction, object proposal generation entails identifying areas in the image that may contain objects. Techniques like selective search are commonly used to generate potential object proposals.
- Object Classification: Once the object proposals are generated, the next step is to classify them as containing an object of interest or not. This classification is typically performed using machine learning algorithms like support vector machines (SVM).
- Bounding Box Regression: After classifying the object proposals, refining the bounding boxes around the objects of interest to accurately determine their location and size is essential. Bounding box regression adjusts the boxes to encompass the target objects.
DETR: A Transformer-Based Revolution
DETR (Detection Transformer) represents a deep learning architecture introduced as an innovative approach to object detection and panoptic segmentation. DETR is a groundbreaking method for object detection that introduces several unique features.
End-to-End Deep Learning Solution
DETR is an end-to-end trainable deep learning architecture for object detection that employs a transformer block. The model takes an image as input and produces a set of bounding boxes and class labels for each object query. It replaces the complex pipeline of manually designed components with a single end-to-end neural network, simplifying the entire process and enhancing understanding.
Streamlined Detection Pipeline
DETR (Detection Transformer) is unique in that it heavily relies on transformers without incorporating certain standard components found in traditional detectors, such as anchor boxes and Non-Maximum Suppression (NMS).
