物体検出
各物体を枠で囲み、種類を答える
本ページの本文は英語で提供されています。タイトルと導入は日本語化されています。
この能力とは何か
Takes an image and outputs a set of bounding boxes, each with a class label and a confidence score. Unlike classification it is not content with one label for the whole image but must say what is where; unlike segmentation it gives rectangles rather than pixel-accurate outlines. Several instances of the same class can be detected at once.
技術的にどう実現するか
The mainstream is single-stage detection: anchors or centre points are densely predicted on a feature map and one forward pass regresses both box location and class at once, the YOLO line and SSD being the classic examples, fast enough for real time. Two-stage detectors first propose regions then classify each, slightly more accurate but slower. The loss optimises localisation and classification together, and non-maximum suppression merges overlapping boxes at the end.
代表的な製品
4関連する組織
代表的な用途
- Vehicle and pedestrian perception for driving
- Video surveillance and perimeter alerts
- Shelf and inventory counting in retail
- Object surveys in remote-sensing imagery
どう評価するか
- mAP
- Mean of per-class average precision, usually at an IoU threshold
- IoU
- Intersection over union between predicted and true boxes, the hit criterion
- Inference speed (FPS)
- As important as accuracy in real-time settings
限界と難しさ
- Recall drops sharply for occluded, truncated and very small objects
- In crowded scenes non-maximum suppression wrongly removes neighbours; two people side by side may become one
- Objects outside the trained classes are either missed or forced into the nearest known class