Object Localization with Classification in Real-Time

Figure 1.1: Object Localization with Classification
Figure 1.2
Figure 1.3 [2]
Figure 1.4: Concept of the grid cell and midpoint
Figure 1.5: Output Format
Figure 1.6 Model Output [5]
Figure 1.7
Figure 1.8
Figure 1.9
Figure 1.10 Image with unnecessary bounding boxes
  1. Discard all the bounding boxes having pc < 0.6, as shown in Figure 1.11.
Figure 1.11
  • Pick the box having the largest pc as the prediction.
  • Discard any remaining box with IOU ≥ 0.5 with the selected box in the previous step.
Figure 1.12 Final output with necessary boxes
Figure 1.13
Figure 1.14
Figure 1.15 [6]
Figure 1.16 [7]
Figure 1.17
Figure 1.18
YOLO can detect fast-moving objects as well as objects that are too close



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Qualcomm| Indian Institute of Technology, Bhubaneswar| Passionate about Deep Learning| https://shobhiit.me/