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Traditional neural objection detection methods use multi-scale features thatallow multiple detectors to perform detecting tasks independently and inparallel. At the same time, with the handling of the prior box, the algorithm'sability to deal with scale invariance is enhanced. However, too many priorboxes and independent detectors will increase the computational redundancy ofthe detection algorithm. In this study, we introduce Dubox, a new one-stageapproach that detects the objects without prior box. Working with multi-scalefeatures, the designed dual scale residual unit makes dual scale detectors nolonger run independently. The second scale detector learns the residual of thefirst. Dubox has enhanced the capacity of heuristic-guided that can furtherenable the first scale detector to maximize the detection of small targets andthe second to detect objects that cannot be identified by the first one.Besides, for each scale detector, with the new classification-regressionprogressive strapped loss makes our process not based on prior boxes.Integrating these strategies, our detection algorithm has achieved excellentperformance in terms of speed and accuracy. Extensive experiments on the VOC,COCO object detection benchmark have confirmed the effectiveness of thisalgorithm.

DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors