Damage to road pavement can develop into cracks, potholes, spallings, and
other issues posing significant challenges to the integrity, safety, and
durability of the road structure. Detecting and monitoring the evolution of
these damages is crucial for maintaining the condition and structural health of
road infrastructure. In recent years, researchers have explored various
data-driven methods for image-based damage detection in road monitoring
applications. The field gained attention with the introduction of the Road
Damage Detection Challenge (RDDC2018), encouraging competition in developing
object detectors on street-view images from various countries. Leading teams
have demonstrated the effectiveness of ensemble models, mostly based on the
YOLO and Faster R-CNN series. Data augmentations have also shown benefits in
object detection within the computer vision field, including transformations
such as random flipping, cropping, cutting out patches, as well as
cut-and-pasting object instances. Applying cut-and-paste augmentation to road
damages appears to be a promising approach to increase data diversity. However,
the standard cut-and-paste technique, which involves sampling an object
instance from a random image and pasting it at a random location onto the
target image, has demonstrated limited effectiveness for road damage detection.
This method overlooks the location of the road and disregards the difference in
perspective between the sampled damage and the target image, resulting in
unrealistic augmented images. In this work, we propose an improved
Cut-and-Paste augmentation technique that is both content-aware (i.e. considers
the true location of the road in the image) and perspective-aware (i.e. takes
into account the difference in perspective between the injected damage and the
target image).

我们提出了一种改进的 “剪切 - 粘贴” 增强技术，既考虑了图像中道路的真实位置，又考虑了注入损伤和目标图像之间的透视差异。

精确剪切粘贴：一种内容和角度感知的道路损伤检测数据增强方法

Cut-and-Paste with Precision: a Content and Perspective-aware Data  Augmentation for Road Damage Detection

Satellite imagery is crucial for tasks like environmental monitoring and
urban planning. Typically, it relies on semantic segmentation or Land Use Land
Cover (LULC) classification to categorize each pixel. Despite the advancements
brought about by Deep Neural Networks (DNNs), their performance in segmentation
tasks is hindered by challenges such as limited availability of labeled data,
class imbalance and the inherent variability and complexity of satellite
images. In order to mitigate those issues, our study explores the effectiveness
of a Cut-and-Paste augmentation technique for semantic segmentation in
satellite images. We adapt this augmentation, which usually requires labeled
instances, to the case of semantic segmentation. By leveraging the connected
components in the semantic segmentation labels, we extract instances that are
then randomly pasted during training. Using the DynamicEarthNet dataset and a
U-Net model for evaluation, we found that this augmentation significantly
enhances the mIoU score on the test set from 37.9 to 44.1. This finding
highlights the potential of the Cut-and-Paste augmentation to improve the
generalization capabilities of semantic segmentation models in satellite
imagery.

卫星影像重要用于环境监测和城市规划，本研究探索了一个适用于语义分割的剪切粘贴增强技术，通过利用语义分割标签中的联通组件，从而在训练过程中随机粘贴提取的实例，实验结果表明，这种增强技术显著提升了语义分割模型的平均交并比得分，从 37.9 提升至 44.1，彰显了其在卫星影像中提高模型泛化能力的潜力。