Tags: Computer Vision, Object Detection, DDSSD, SSD, Dilated Convolution, Deconvolution, Deep Learning, Small Object Detection Reading Time: ~14 minutes If you've spent any time working with object detectors in the real world — not just running demo notebooks, but actually deploying them on real images — you know that detecting small objects is genuinely painful. A pedestrian 200 meters away. A bolt on a conveyor belt. A bird in a surveillance frame. These aren't exotic edge cases; they come up constantly, and most detectors handle them terribly. SSD (Single Shot MultiBox Detector) is one of the fastest and most widely deployed detectors out there. But it has a well-known weakness: small objects. The features SSD uses to detect small things are shallow in the network, and shallow features are informationally thin — they haven't seen enough context to make confident predictions. DDSSD — Dilation and Deconvolution Single Shot MultiBox Detector — was built to fix exactl...