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This paper considers an architecture referred to as Cascade Region ProposalNetwork (Cascade RPN) for improving the region-proposal quality and detectionperformance by \textit{systematically} addressing the limitation of theconventional RPN that \textit{heuristically defines} the anchors and\textit{aligns} the features to the anchors. First, instead of using multipleanchors with predefined scales and aspect ratios, Cascade RPN relies on a\textit{single anchor} per location and performs multi-stage refinement. Eachstage is progressively more stringent in defining positive samples by startingout with an anchor-free metric followed by anchor-based metrics in the ensuingstages. Second, to attain alignment between the features and the anchorsthroughout the stages, \textit{adaptive convolution} is proposed that takes theanchors in addition to the image features as its input and learns the sampledfeatures guided by the anchors. A simple implementation of a two-stage CascadeRPN achieves AR 13.4 points higher than that of the conventional RPN,surpassing any existing region proposal methods. When adopting to Fast R-CNNand Faster R-CNN, Cascade RPN can improve the detection mAP by 3.1 and 3.5points, respectively. The code is made publicly available at\url{https://github.com/thangvubk/Cascade-RPN.git}.

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution