Up 物体検出 (Classification with Localization) 作成: 2021-05-02
更新: 2021-05-07

  • Deep Learning based Object Detection using YOLOv3 ‥‥" より：
    　 In traditional computer vision approaches, a sliding window was used to look for objects at different locations and scales. Because this was such an expensive operation, the aspect ratio of the object was usually assumed to be fixed.
    Early Deep Learning based object detection algorithms like the R-CNN and Fast R-CNN used a method called Selective Search to narrow down the number of bounding boxes that the algorithm had to test.
    Another approach called Overfeat involved scanning the image at multiple scales using sliding windows-like mechanisms done convolutionally.
    This was followed by Faster R-CNN that used a Region Proposal Network (RPN) for identifying bounding boxes that needed to be tested. By clever design the features extracted for recognizing objects, were also used by the RPN for proposing potential bounding boxes thus saving a lot of computation.
    YOLO on the other hand approaches the object detection problem in a completely different way. It forwards the whole image only once through the network.
    SSD is another object detection algorithm that forwards the image once though a deep learning network, but YOLOv3 is much faster than SSD while achieving very comparable accuracy.
    YOLOv3 gives faster than realtime results on a M40, TitanX or 1080 Ti GPUs.

  • 「YOLO：ディープラーニングによる一般物体検出手法」より：
    　 物体検出の処理時間が，画像1枚あたり約22ms
    ── Faster R-CNN の6〜7倍ほどの高速化を実現