CFM: A Consistency Filtering Mechanism for Road Damage Detection

Zixiang Pei, Rongheng Lin, Xiubao Zhang, Haifeng Shen, Jian Tang, Yi Yang

Research output: Chapter in Book/Entry/PoemConference contribution

17 Scopus citations


This article presents the solution that we use in the Global Road Damage Detection Challenge 2020, which is designed to recognize the road damages present in an image captured from three countries: India, Japan, and Czech. In this challenge, Cascade R-CNN is selected as a baseline model to detect objects in images. It is commonly known that making a precise annotation in a large dataset is crucial to the performance of object detection and placing bounding boxes for every object in each image is time-consuming and costs a lot. To make full use of available unlabeled data, the consistency filtering mechanism (CFM) with self-supervised methods is proposed to utilize high-confident samples with pseudo-labels for training. And we also apply a series of data augmentation techniques (road segmentation, flip, mixup, CLAHE) to labeled data in training phase. Moreover, we ensemble models with different tricks by weighted boxes fusion to produce the final prediction. Finally, our proposed method can achieve a great mean f1-score of 0.6290 on the test1 dataset and 0.6219 on the test2 dataset respectively, which wins the Bronze Prize (ranks 3rd place). Code and trained models are available at the following link:, password: xzc6.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728162515
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020


Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta


  • cascade r-cnn
  • consistency filtering mechanism
  • data augmentation
  • model fusion
  • road damage detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


Dive into the research topics of 'CFM: A Consistency Filtering Mechanism for Road Damage Detection'. Together they form a unique fingerprint.

Cite this