方法对比

	目标检测	图像分类	语义分割
领域	深度学习	深度学习	深度学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2014–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2015
提出者	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Long, J., Shelhamer, E., & Darrell, T.
类型	Supervised deep learning (region proposal or single-shot)	Supervised classification task	Dense prediction / pixel-wise classification
开创性文献	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.
别名	visual object detection, image object localization, region-based object detection, bounding-box detection	visual classification, image recognition, CNN-based classification, visual categorization	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
相关	3	5	5
摘要	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.