论文标题
通过检测和更快的R-CNN进行道路损坏检测和分类
Road Damage Detection and Classification with Detectron2 and Faster R-CNN
论文作者
论文摘要
这条路对于生活的许多方面至关重要，道路维护对于人类安全至关重要。允许及时修复道路损失的关键任务之一是快速有效地检测和对其进行分类。这项工作详细介绍了这些任务评估的策略和实验。具体而言，我们使用不同的基本模型和配置来评估DestectRon2对更快的R-CNN的实现。我们还使用全球道路损坏检测挑战2020尝试了这些方法，这是IEEE Big Data 2020 Big Data Cup挑战数据集中的曲目。结果表明，具有detectron2的默认配置的更快的R-CNN的X101-FPN基本模型非常有效，并且足以在此挑战下转移到不同国家。对于Test1和Test2挑战集，这种方法的F1得分分别为51.0％和51.4％。尽管可视化结果显示出良好的预测结果，但F1得分较低。因此，我们还针对现有注释评估了预测结果，并发现一些差异。因此，我们还提出了改进该数据集的标签过程的策略。
The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2's implementation of Faster R-CNN using different base models and configurations. We also experiment with these approaches using the Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge dataset. The results show that the X101-FPN base model for Faster R-CNN with Detectron2's default configurations are efficient and general enough to be transferable to different countries in this challenge. This approach results in F1 scores of 51.0% and 51.4% for the test1 and test2 sets of the challenge, respectively. Though the visualizations show good prediction results, the F1 scores are low. Therefore, we also evaluate the prediction results against the existing annotations and discover some discrepancies. Thus, we also suggest strategies to improve the labeling process for this dataset.
