Linganisha mbinu

	Utambuzi wa vitu vingi (Multimodal Object Detection)	Utambuzi wa Kitu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2015–2019	2014–2016
Mwanzilishi	Multiple contributors (e.g., Chen & Deng, Liang et al.)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Aina	Fusion-based deep detection	Supervised deep learning (region proposal or single-shot)
Chanzo asilia	Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965.	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.
Majina mbadala	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
Zinazohusiana	6	3
Muhtasari	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.