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| Deteksi Objek Multimodus× | Pengesanan Objek× | |
|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2015–2019 | 2014–2016 |
| Pengasas≠ | Multiple contributors (e.g., Chen & Deng, Liang et al.) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| Jenis≠ | Fusion-based deep detection | Supervised deep learning (region proposal or single-shot) |
| Sumber perintis≠ | Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965. link ↗ | 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 ↗ |
| Alias | multi-sensor object detection, cross-modal detection, RGB-D object detection, fusion-based object detection | visual object detection, image object localization, region-based object detection, bounding-box detection |
| Berkaitan≠ | 6 | 3 |
| Ringkasan≠ | Multimodal object detection extends single-modality object detectors by jointly processing signals from multiple sensor types — such as RGB cameras, depth sensors, LiDAR, radar, or text descriptions — to localize and classify objects with higher accuracy and robustness than any single modality alone. Fusion of complementary information is the core design principle. | 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. |
| ScholarGateSet data ↗ |
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