Comparar métodos

	Segmentación de instancias	Detección de objetos
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2017	2014–2016
Autor original	He, K., Gkioxari, G., Dollar, P., Girshick, R.	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Tipo	Pixel-level detection and mask prediction	Supervised deep learning (region proposal or single-shot)
Fuente seminal	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	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.
Alias	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation	visual object detection, image object localization, region-based object detection, bounding-box detection
Relacionados	4	3
Resumen	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.	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.