  Recently, significant progresses have been made in object detection on common
benchmarks (i.e., Pascal VOC). However, object detection in real world is still
challenging due to the serious data imbalance. Images in real world are
dominated by easy samples like the wide range of background and some easily
recognizable objects, for example. Although two-stage detectors like Faster
R-CNN achieved big successes in object detection due to the strategy of
extracting region proposals by region proposal network, they show their poor
adaption in real-world object detection as a result of without considering
mining hard samples during extracting region proposals. To address this issue,
we propose a Cascade framework of Region Proposal Networks, referred to as
C-RPNs. The essence of C-RPNs is adopting multiple stages to mine hard samples
while extracting region proposals and learn stronger classifiers. Meanwhile, a
feature chain and a score chain are proposed to help learning more
discriminative representations for proposals. Moreover, a loss function of
cascade stages is designed to train cascade classifiers through
backpropagation. Our proposed method has been evaluated on Pascal VOC and
several challenging datasets like BSBDV 2017, CityPersons, etc. Our method
achieves competitive results compared with the current state-of-the-arts and
all-sided improvements in error analysis, validating its efficacy for detection
in real world.
