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Object Detection: A Quick Overview

Introduction

Object detection is a critical task in computer vision, involving the classification and localization of objects within an image or video. This manual provides a quick overview of various methods to enhance the efficiency and accuracy of object detection. We'll explore different categories of object detection, including Faster R-CNN, YOLO (You Only Look Once), CenterNet, and DETR (DEtection Transformer). Finally, we'll delve into open-set object detection and object detection with limited data.

Categories of Object Detection Methods

Object detection methods can be categorized based on key attributes influencing their design and performance. Here are prominent categories:

  • Two-Stage vs. One-Stage Methods:
  • Two-stage methods, like Faster R-CNN, involve region proposal and subsequent classification, offering high accuracy but may be slower.

  • One-stage methods, such as YOLO, perform object detection in a single step, providing faster inference for real-time applications.

  • Anchor-Based vs. Anchor-Free Methods:

  • Anchor-based methods, like Faster R-CNN, use predefined anchor boxes, while anchor-free methods, such as CenterNet, eliminate the need for predefined anchors.

  • Region-Based vs. Query-Based Methods:

  • Region-based methods divide an image into regions (e.g., Faster R-CNN), while query-based methods like DETR use transformer architectures for set prediction.

  • Plain vs. Hierarchical Methods:

  • Plain methods maintain a single-scale feature map, e.g., ViTDet, while hierarchical methods contain multi-scale features.

Two-Stage Methods: Faster R-CNN

Faster R-CNN (Region-based Convolutional Neural Network):

  • Overview: A two-stage framework combining region proposal and object classification using a region proposal network (RPN).

  • Links: R-CNN, Fast R-CNN, Faster R-CNN

One-Stage Methods: YOLO

YOLO (You Only Look Once):

  • Overview: A one-stage algorithm dividing the image into a grid and predicting bounding boxes and class probabilities directly for real-time object detection.

  • Links: YOLO, YOLO brief history

Anchorless Methods: CenterNet

CenterNet:

  • Overview: An anchorless approach focusing on predicting object centers and regressing bounding box coordinates directly, eliminating the need for predefined anchors.

  • Links: CenterNet

Transformer-Based Methods: DETR

DETR (DEtection Transformer):

  • Overview: A transformer-based object detection model formulating object detection as a set prediction problem, simultaneously predicting object classes and bounding box coordinates.

  • Links: LW-DETR, RT-DETR, D-FINE, DETR, deformableDETR

Open-Set Object Detection | Open Vocabulary Object Detection (OVD)

  • Overview: Open-set object detection, or open vocabulary object detection, aims to detect objects of novel categories beyond the training vocabulary. Traditional models are limited to a fixed set, but open-set detection scales up the vocabulary size.

  • Links: Grounding DINO, OWL-VIT, Detic, paperwithcode list.

Object Detection with Limited Data

To address the challenge of limited labeled data, leveraging pre-training in self-supervised learning is an effective strategy. Two prominent methods are contrastive learning and reconstruction-based methods. In contrastive learning, data augmentation is applied, and the model learns by bringing the representation of augmented parts together while pushing non-augmented parts further apart. Another method involves removing part of the data, and the model attempts to reconstruct the missing portion, as seen in Masked Autoencoders (MAE).

Alternatively, foundation models—large models trained on extensive datasets—can serve as a pre-training step. These pre-trained models can be fine-tuned on specific tasks using a smaller dataset or used to distill knowledge into a smaller model, minimizing size while preserving performance.

Another common approach involves training with different modalities, particularly text and image data, in a self-supervised manner. Following the success of models like CLIP, various methods, such as Grounding DINO and OWL-VIT, have adopted this approach for training.