SSD: Real-Time Detection Without Region Proposals
SSD scores default boxes across multiple scales in one forward pass. It runs real-time robotics and mobile vision where two-stage detectors lag. The footgun is ignoring shallow feature maps, which destroys small object accuracy as early layers carry fine…
SSD turns object detection into a single forward pass by tiling the image with default boxes at multiple scales and aspect ratios, then scoring and refining them at once. It eliminates separate proposal generation and feature resampling, making it ideal for real-time robotics and mobile vision where Faster R-CNN is too slow. The footgun is ignoring shallow feature maps, which destroys small-object accuracy because the architecture relies on early high-resolution layers to supply fine detail that deep layers lose.
Read the original → arXiv
- #computer vision
- #object detection
- #deep learning
- #real-time
- #neural networks
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