Traditional neural objection detection methods use multi-scale features that
allow multiple detectors to perform detecting tasks independently and in
parallel. At the same time, with the handling of the prior box, the algorithm's
ability to deal with scale invariance is enhanced. However, too many prior
boxes and independent detectors will increase the computational redundancy of
the detection algorithm. In this study, we introduce Dubox, a new one-stage
approach that detects the objects without prior box. Working with multi-scale
features, the designed dual scale residual unit makes dual scale detectors no
longer run independently. The second scale detector learns the residual of the
first. Dubox has enhanced the capacity of heuristic-guided that can further
enable the first scale detector to maximize the detection of small targets and
the second to detect objects that cannot be identified by the first one.
Besides, for each scale detector, with the new classification-regression
progressive strapped loss makes our process not based on prior boxes.
Integrating these strategies, our detection algorithm has achieved excellent
performance in terms of speed and accuracy. Extensive experiments on the VOC,
COCO object detection benchmark have confirmed the effectiveness of this
algorithm