In the domain of traffic safety and road maintenance, precise detection of
road damage is crucial for ensuring safe driving and prolonging road
durability. However, current methods often fall short due to limited data.
Prior attempts have used Generative Adversarial Networks to generate damage
with diverse shapes and manually integrate it into appropriate positions.
However, the problem has not been well explored and is faced with two
challenges. First, they only enrich the location and shape of damage while
neglect the diversity of severity levels, and the realism still needs further
improvement. Second, they require a significant amount of manual effort. To
address these challenges, we propose an innovative approach. In addition to
using GAN to generate damage with various shapes, we further employ texture
synthesis techniques to extract road textures. These two elements are then
mixed with different weights, allowing us to control the severity of the
synthesized damage, which are then embedded back into the original images via
Poisson blending. Our method ensures both richness of damage severity and a
better alignment with the background. To save labor costs, we leverage
structural similarity for automated sample selection during embedding. Each
augmented data of an original image contains versions with varying severity
levels. We implement a straightforward screening strategy to mitigate
distribution drift. Experiments are conducted on a public road damage dataset.
The proposed method not only eliminates the need for manual labor but also
achieves remarkable enhancements, improving the mAP by 4.1% and the F1-score by
4.5%.

交通安全和道路维护领域中，准确检测道路损伤对确保安全驾驶和延长道路耐久性至关重要。本研究提出了一种创新的方法，利用生成对抗网络生成具有不同形状的损伤，并通过纹理合成技术提取道路纹理，通过控制合成损伤的严重程度，嵌入到原始图像中，从而实现了损伤严重程度的丰富性和与背景的更好对齐，同时提高了 mAP 和 F1-score。

融合 GAN 和纹理合成以增强道路损伤检测

Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection

With the rapid development of global road transportation, countries worldwide
have completed the construction of road networks. However, the ensuing
challenge lies in the maintenance of existing roads. It is well-known that
countries allocate limited budgets to road maintenance projects, and road
management departments face difficulties in making scientifically informed
maintenance decisions. Therefore, integrating various artificial intelligence
decision-making techniques to thoroughly explore historical maintenance data
and adapt them to the context of road maintenance scientific decision-making
has become an urgent issue. This integration aims to provide road management
departments with more scientific tools and evidence for decision-making. The
framework proposed in this paper primarily addresses the following four issues:
1) predicting the pavement performance of various routes, 2) determining the
prioritization of maintenance routes, 3) making maintenance decisions based on
the evaluation of the effects of past maintenance, and considering
comprehensive technical and management indicators, and 4) determining the
prioritization of maintenance sections based on the maintenance effectiveness
and recommended maintenance effectiveness. By tackling these four problems, the
framework enables intelligent decision-making for the optimal maintenance plan
and maintenance sections, taking into account limited funding and historical
maintenance management experience.

利用人工智能决策技术，通过预测道路性能、确定维护路线的优先级、基于过去维护效果评估和综合技术和管理指标进行维护决策、以及基于维护效果和推荐维护效果确定维护区段的优先级，实现了有限经费和历史维护管理经验的智能决策，提供科学工具和证据来帮助道路管理部门制定最佳维护计划和维护区段。