Comparar métodos

	Detecção de Objetos	Segmentação de Instância	Segmentação semântica
Área	Aprendizado profundo	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2014–2016	2017	2015
Autor original	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Long, J., Shelhamer, E., & Darrell, T.
Tipo	Supervised deep learning (region proposal or single-shot)	Pixel-level detection and mask prediction	Dense prediction / pixel-wise classification
Fonte seminal	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.	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.
Outros nomes	visual object detection, image object localization, region-based object detection, bounding-box detection	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Relacionados	3	4	5
Resumo	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.	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.