object detection编辑历史

Object detection is a computer vision technique that involves identifying and locating instances of specific objects within digital images or videos. It combines classification (what object is present) and localization (where the object is) to output bounding boxes and class labels. Object detection is a foundational task in information technology, enabling applications such as autonomous driving, surveillance, medical imaging, and augmented reality. Modern approaches predominantly rely on deep learning, with architectures like convolutional neural networks (CNNs) and transformer-based models.

## 1 Historical development

### 1.1 Early feature-based methods

#### 1.1.1 Haar cascades
Haar cascades, introduced by Viola and Jones in 2001, were among the first real-time object detection systems. They use Haar-like features—simple rectangular filters—to capture contrast patterns (e.g., edges, lines). A cascade of classifiers eliminates negative regions quickly, making detection fast. Haar cascades were primarily used for face detection and remained popular in embedded systems until the 2010s.

#### 1.1.2 Histogram of oriented gradients (HOG)
HOG, proposed by Dalal and Triggs in 2005, computes gradient orientation histograms over local image cells. Combined with a support vector machine (SVM) classifier, HOG achieved high accuracy for pedestrian detection. It was a key feature-based method before deep learning, but its performance degraded under occlusion and varied lighting.

### 1.2 Rise of deep learning

#### 1.2.1 Region-based CNN (R-CNN)
R-CNN, introduced by Girshick et al. in 2014, was a breakthrough. It uses selective search to generate candidate regions, then feeds each region through a CNN for classification and bounding-box regression. While accurate, R-CNN was slow because each region required a separate forward pass.

#### 1.2.2 Fast and Faster R-CNN
Fast R-CNN (2015) improved speed by processing the entire image once through a CNN and using a region-of-i