TY - GEN
T1 - CFM
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
AU - Pei, Zixiang
AU - Lin, Rongheng
AU - Zhang, Xiubao
AU - Shen, Haifeng
AU - Tang, Jian
AU - Yang, Yi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - 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: https://pan.baidu.com/s/1VjLuNBVJGS34mMMpDkDRGQ, password: xzc6.
AB - 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: https://pan.baidu.com/s/1VjLuNBVJGS34mMMpDkDRGQ, password: xzc6.
KW - cascade r-cnn
KW - consistency filtering mechanism
KW - data augmentation
KW - model fusion
KW - road damage detection
UR - http://www.scopus.com/inward/record.url?scp=85103847984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103847984&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9377911
DO - 10.1109/BigData50022.2020.9377911
M3 - Conference contribution
AN - SCOPUS:85103847984
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 5584
EP - 5591
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2020 through 13 December 2020
ER -