Abstract
The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However, the complexity of object’s shape and orientation exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in any dataset as well as real im- ages. In this project, I embrace this observation and introduce the Faster Rotated Region-based Convolutional Neural Network (Faster RR-CNN), which can learn and accurately extract features of rotated regions and arbitrary-oriented objects precisely. In comparison with the classic Faster RCNN, Faster RR-CNN has three important new components including a skew non-maximum suppression, a rotated bounding box regression model and a rotated region of interest (RRoI) pooling layer. I conduct experiments using the PASCAL VOC 2012 dataset, demonstrat-ing the potential ability of this novel network in detecting oriented objects.
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