# C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks

## TL;DR

This paper introduces C-RPNs, a cascade framework for region proposal networks that enhances real-world object detection by mining hard samples and learning stronger classifiers, showing improved performance on multiple datasets.

## Contribution

The paper proposes a novel cascade structure of RPNs with feature and score chains, and a specialized loss function, to better handle data imbalance and hard samples in real-world detection tasks.

## Key findings

- Achieves competitive results on Pascal VOC and challenging datasets.
- Demonstrates all-sided improvements in error analysis.
- Validates effectiveness for real-world object detection.

## Abstract

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.