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.