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| Deteksi Objek× | Semantic Segmentation× | |
|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2014–2016 | 2015 |
| Pencetus≠ | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) | Long, J., Shelhamer, E., & Darrell, T. |
| Tipe≠ | Supervised deep learning (region proposal or single-shot) | 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. DOI ↗ | 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. DOI ↗ |
| Alias | visual object detection, image object localization, region-based object detection, bounding-box detection | pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation |
| Terkait≠ | 3 | 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. | 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. |
| ScholarGateSet data ↗ |
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