เปรียบเทียบวิธี

	การตรวจจับวัตถุที่อธิบายได้	การตรวจจับวัตถุ
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2016–2017	2014–2016
ผู้ริเริ่ม	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
ประเภท	Post-hoc explainability applied to object detection	Supervised deep learning (region proposal or single-shot)
แหล่งต้นตำรับ	Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.	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.
ชื่อเรียกอื่น	XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD	visual object detection, image object localization, region-based object detection, bounding-box detection
ที่เกี่ยวข้อง	5	3
สรุป	Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans.	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.