Bandingkan kaedah

	Pengesanan Objek	Klasifikasi Imej	Segmentasi Instans	Semantic Segmentation
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning	Machine learning	Machine learning
Tahun asal	2014–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2017	2015
Pengasas	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Long, J., Shelhamer, E., & Darrell, T.
Jenis	Supervised deep learning (region proposal or single-shot)	Supervised classification task	Pixel-level detection and mask prediction	Dense prediction / pixel-wise classification
Sumber perintis	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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	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.
Alias	visual object detection, image object localization, region-based object detection, bounding-box detection	visual classification, image recognition, CNN-based classification, visual categorization	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Berkaitan	3	5	4	5
Ringkasan	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.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.	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.