Comparer des méthodes

	Détection d'objets	Segmentation d'instances
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014–2016	2017
Auteur d'origine	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Type	Supervised deep learning (region proposal or single-shot)	Pixel-level detection and mask prediction
Source fondatrice	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.
Alias	visual object detection, image object localization, region-based object detection, bounding-box detection	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Apparentées	3	4
Résumé	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.