# Vision And YOLO Literature

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## YOLO History And Paradigms

Status: `needs confirmation`

## Review Questions

* What makes one-stage detection suitable for real-time candidate generation?
* How should `bbox`, `class`, and `confidence` be transformed into BCI command candidates?
* How should candidates be filtered, tracked, frozen, and confirmed?
* What failure modes matter for BCI selection and robot grasping?
* What additional method is needed for grasp-pose estimation beyond object detection?

## Evidence To Extract

| Evidence Need | Candidate IDs | Status |
| --- | --- | --- |
| YOLO real-time detection baseline | YOLO-001 | text extracted |
| YOLO evolution | YOLO-002, YOLO-003 | text extracted |
| two-stage comparator | YOLO-004 | text extracted |
| dataset/context evidence | YOLO-005 | text extracted |
| dense detector comparator | YOLO-007 | text extracted |
| modern YOLO variants | YOLO-009, YOLO-010 | text extracted |
| RGB-D / hand-eye / grasp pose | GRASP-001, GRASP-003 to GRASP-007 | text extracted |