Object detection is a computer vision task used to identify object categories in images or videos and simultaneously determine the location of the objects. This technique combines object classification and spatial localization capabilities, requiring not only to determine "what is in" an image but also "where it is." It typically achieves a structured understanding of visual scenes by predicting object categories, bounding boxes, or other spatial information, and is one of the important foundational tasks in the field of computer vision.

The development of object detection is built upon long-term research in multiple fields, including image classification, object localization, and traditional computer vision. Early methods primarily relied on sliding windows, manually designed features (such as Haar and HOG), and combinations of classifiers to complete object search, but their computational efficiency and adaptability to complex scenes were limited. With the development of deep learning, researchers began to utilize convolutional neural networks to automatically learn visual features. In 2014, researchers Ross Girshick et al. from the University of California, Berkeley, published a paper... Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation The R-CNN method was proposed, which introduces deep convolutional features into the object detection task, significantly improving the detection performance and becoming one of the important representative studies in the field of deep learning object detection.

Modern object detection systems typically rely on technologies such as convolutional neural networks (CNNs), Transformers, and basic vision models to classify and locate objects in images. Depending on the detection method, they can be categorized into two-stage detection methods (such as Faster R-CNN) and single-stage detection methods (such as YOLO and SSD), and are widely used in scenarios such as autonomous driving, intelligent monitoring, robot perception, industrial inspection, and medical image analysis. In recent years, the detection paradigm has also been evolving: end-to-end Transformer detectors, represented by DETR, eliminate manual post-processing steps such as anchor box design and non-maximum suppression; open-vocabulary detection methods combined with vision-language models enable the system to identify object categories not seen during training, further expanding the application boundaries of object detection.