BR-AsPavDam - Brazilian Asphalt Pavement Damage Dataset

BR-AsPavDam: A public urban asphalt-defects dataset collected in Brazil with a bumper-mounted smartphone. Images include varied lighting, traffic and surface conditions. Five classes follow DNIT guidelines: Fissures, Shoving (rutting/shoving), Ravelling, Pothole, Patch. Labels were curated by a technical committee with quality checks. YOLO-format annotations (one TXT per image, class x_center y_center width height, normalized). High-resolution image dataset of defects in urban asphalt pavement in Brazil, annotated in YOLO format. Each image is accompanied by a .txt file with normalized bounding boxes (cx cy w h). Classes 0. Fissures Shoving Ravelling Pothole Patch Structure: images/, labels/, splits train/val/test. Resolution: high-res originals; filenames are stable; checksums provided. Intended use: benchmarking defect detection and public-works planning. Known limitations: class imbalance, weather and location bias. Related resources: Source code & training recipes (GitHub), thesis with methods and metrics (mAP@50 = 0.844 for best model). License: CC-BY 4.0 (data). Please cite the dataset and repository; BibTeX provided below. How to reproduce: see GitHub for enviroment, scripts, and release tags matching this version. @dataset{Borges_BR-AsPavDam_2025, title = {BR-AsPavDam: Urban asphalt defects dataset (Brazil)}, author = {Borges, Marcos A. and Spanhol, Fabio}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.16291115}, url = {https://doi.org/10.5281/zenodo.16291115}}____________More details: Field Data Acquisition The data were collected using a video-capture device (Apple iPhone 15) mounted on the front bumper of a passenger vehicle that drove along urban roads in Cascavel, Paraná, Brazil. Recording was started and stopped manually on the device. Whenever possible, the vehicle speed was kept at 40 km/h while maintaining a safe distance from the vehicle ahead. Recording settings (native Camera app): Resolution: 4K (3840 × 2160 px), capturing fine-grained surface details (e.g., fissures and irregularities) Frame rate: 60 FPS, to ensure fluid motion appropriate for frame extraction Exposure & lighting: natural daylight, spanning sunny and shaded segments Aperture / ISO / Focus: f/1.8, ISO 100, continuous autofocus, optical image stabilization, auto white balance Data collection took place between 09:30 and 14:30, to ensure adequate illumination. Routes were chosen to include clear occurrences of asphalt distresses, maximizing the likelihood of capturing diverse defect types within the available driving time. Surveyed road segments (urban network of Cascavel-PR): Segment 1: Rua Vitória, Rua Barão do Cerro Azul, Rua São Paulo — ~3.04 km Segment 2: Rua Rio Grande do Sul, Rua Erechim — ~3.44 km Segment 3: Rua Paraná — ~2.10 km Segment 4: Rua Natal — ~1.04 km Segment 5: Rua João Lili Cirico, Rua Teresina, Rua Rui Barbosa, Rua São Luís, Rua Visconde do Rio Branco — ~3.61 km The total route length was approximately 13.22 km, spanning central arteries with higher flows and heavier vehicles (Segments 1–3) and residential/local streets with two-way traffic and lighter flow (Segments 4–5). The segments were selected by municipal civil engineers based on prior knowledge of problematic locations, ensuring that all normative defect types targeted in this study were represented despite the modest overall distance. Quality Control (Frame Extraction) After field acquisition, video files were transferred to an Apple MacBook (M2). Static images were extracted using VLC(frame capture), preserving the original quality, and saved as PNG. To improve visibility and variety, frames with very near vehicles, traffic lights stops, or heavy congestion were avoided. To reduce redundancy, screen captures were taken at ~2-second intervals; combined with the target speed (≈ 40 km/h), this spacing limits near-duplicate imagery. A total of 3,565 images were initially extracted; not all were retained, as some contained no defects. Annotation Annotations were produced with CVAT by specialists in asphalt pavements. The labeling committee included three civil engineers from the Cascavel municipality, all with prior experience in paving projects and knowledge of the Brazilian DNIT standards. Candidate engineers were interviewed regarding practical paving experience and familiarity with the normative criteria. Bounding-box annotations followed DNIT’s defect definitions. During pilot labeling, eight DNIT categories were initially targeted: cracking (fissures), rutting (afundamento), corrugation (ondulação), shoving (escorregamento), bleeding (exsudação), ravelling (desgaste), pothole (buraco), and patching (remendo). Two adjustments were made to improve reliability: Bleeding (exsudação) was removed due to frequent confusion with standing water from rainfall, washing, or leaks. The fully automated diagnosis of this phenomenon would likely require external/auxiliary informationbeyond RGB imagery. Corrugation, shoving, and rutting were merged into a single Shovingclass, given their similar plastic-deformation mechanism and visual overlap under DNIT criteria, and their low frequency in urban settings(heavier-vehicle loads are more common on highways). Disagreements (e.g., box extents, class assignment) were resolved in technical review meetings, achieving consensus on the final labels. Annotations were exported in YOLO format (one TXT per image; normalized <class_id> <x_center> <y_center> <width> <height> with origin at the top-left corner; floating-point decimals, space-separated, no header). BR-AsPavDam (Dataset Summary & Organization) BR-AsPavDam comprises 2,167 images with 3,918 labeled defects, released for academic use. Final classes (after consolidation): Fissures (cracking) Shoving (merged: corrugation, shoving, rutting) Ravelling (loss of aggregate, surface ravel) Pothole Patch (visible maintenance/repair areas) Distribution of bounding boxes (high-resolution source set): Class # Boxes Fissures 1,843 Shoving 14 Ravelling 1,097 Pothole 125 Patch 839 Total 3,918 From the 3,565 initially extracted frames, 2,167 images remained after quality control (non-defective or low-quality frames removed). Folder structure (ready for object detection training): At the dataset root: images/ and labels/. images/ is organized by predominant normative class into five subfolders: Fissures/, Patch/, Pothole/, Ravelling/, Shoving/ (for Deformations). labels/ has no subfolders; for each image <name>.png, there is a homonymous <name>.txt file with YOLO boxes. File naming (predominant-class scheme):<PredominantClass>_NNN.png and <PredominantClass>_NNN.txt.If two or more classes occur with the same frequency in an image, the name follows a severity order (from most to least severe): Pothole > Patch > Shoving > Fissures > Ravelling. Naming reflects the predominant class only; all objects present are fully annotated in the corresponding TXT file. Image & label format: Images: PNG, 1152 × 2048 px (width × height) Labels: YOLO normalized format; one line per instance:<class_id> <x_center> <y_center> <width> <height> Class IDs (0–4):0 = Fissures, 1 = Shoving, 2 = Ravelling, 3 = Pothole, 4 = Patch Notes for reuse & reproducibility:— The dataset adheres to DNIT defect definitions and was curated by an expert labeling committee with consensus review.— Sampling choices (daylight window, ~2 s frame spacing, target 40 km/h, route diversity) were designed to balance visibility and non-redundancy.— Users should be aware of the class imbalance (e.g., few Deformations) typical of urban networks and consider appropriate training strategies (e.g., reweighting, augmentation).BR-AsPavDam：一款于巴西采集的公开城市沥青缺陷数据集，采集方式为将智能手机安装于车辆保险杠上。数据集图像涵盖多样的光照、交通与路面状况，共包含5类遵循巴西国家交通基础设施局（Departamento Nacional de Infraestrutura de Transportes, DNIT）规范的病害：裂缝（Fissures）、推挤变形（Shoving，含车辙/推挤变形）、集料松散（Ravelling）、坑槽（Pothole）与修补区域（Patch）。标注由具备质量审核的技术委员会完成，采用YOLO格式标注（每张图像对应一个TXT文件，标注格式为：类别ID 归一化中心点x坐标 归一化中心点y坐标 归一化宽度 归一化高度）。 本数据集为巴西城市沥青路面缺陷高分辨率图像数据集，采用YOLO格式标注。每张图像均附带一个包含归一化边界框（cx cy w h）的.txt文件。 类别： 0. 裂缝 1. 推挤变形 2. 集料松散 3. 坑槽 4. 修补区域 数据集结构：包含images/、labels/文件夹，以及训练集（train）、验证集（val）、测试集（test）划分。图像为高分辨率原始文件，文件名固定，并提供校验和。 用途：用于缺陷检测模型基准测试与市政工程规划。 已知局限性：存在类别不平衡、天气与采集地点偏差问题。 相关资源：包含源代码与训练配置（GitHub仓库），以及包含研究方法与评测指标的学位论文（最优模型的平均精度均值@交并比阈值0.5（mAP@50）为0.844）。 授权协议：数据采用知识共享署名4.0国际许可协议（CC-BY 4.0）。请引用该数据集与相关仓库，下方提供BibTeX引用格式。 复现方法：请访问GitHub仓库获取对应版本的运行环境、脚本与发布标签。 @dataset{Borges_BR-AsPavDam_2025, title = {BR-AsPavDam: Urban asphalt defects dataset (Brazil)}, author = {Borges, Marcos A. and Spanhol, Fabio}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.16291115}, url = {https://doi.org/10.5281/zenodo.16291115}} 更多详情： ### 现场数据采集 本数据集通过安装于巴西巴拉那州卡斯卡韦尔市城市道路行驶的乘用车前保险杠上的视频采集设备（Apple iPhone 15）获取。采集操作通过设备手动启停，行驶过程中尽可能保持车速为40 km/h，并与前车保持安全车距。 录制参数（原生相机应用）： - 分辨率：4K（3840 × 2160像素），可捕捉路面细粒度细节（如裂缝与凹凸不平） - 帧率：60 FPS，确保运动流畅以适配帧提取需求 - 曝光与光照：采用自然日光，涵盖晴天与阴影路段 - 光圈/ISO/对焦：f/1.8，ISO 100，连续自动对焦，光学图像防抖，自动白平衡 数据采集时间为09:30至14:30，以确保充足的光照。采集路线选择包含明显沥青病害的路段，以在有限行驶时间内最大化捕捉多种缺陷类型的概率。 调研路段（卡斯卡韦尔市城市道路网）： - 路段1：Rua Vitória、Rua Barão do Cerro Azul、Rua São Paulo — 全长约3.04 km - 路段2：Rua Rio Grande do Sul、Rua Erechim — 全长约3.44 km - 路段3：Rua Paraná — 全长约2.10 km - 路段4：Rua Natal — 全长约1.04 km - 路段5：Rua João Lili Cirico、Rua Teresina、Rua Rui Barbosa、Rua São Luís、Rua Visconde do Rio Branco — 全长约3.61 km 总采集路线全长约13.22 km，涵盖车流量与载重车辆较多的城市主干道（路段1-3），以及双向通行、车流量较小的社区街道（路段4-5）。路段由市政土木工程师根据已知病害路段的先验知识选取，确保本研究覆盖所有规范定义的缺陷类型，尽管总采集距离较短。 ### 质量控制（帧提取） 现场采集完成后，视频文件被传输至Apple MacBook（M2）设备。使用VLC媒体播放器的帧捕获功能提取静态图像，保留原始画质，并保存为PNG格式。 为提升图像可见性与多样性，避开了紧邻其他车辆、遇红灯停车或严重拥堵的画面。为减少数据冗余，以约2秒的间隔提取帧；结合目标行驶速度（≈40 km/h），该间隔可有效限制近乎重复的图像。 初始共提取3565张图像，部分无病害的图像未被保留。 ### 标注工作 标注由沥青路面领域专家通过CVAT（计算机视觉标注工具）完成。标注委员会由卡斯卡韦尔市的三名土木工程师组成，均具备路面工程实践经验与巴西DNIT标准相关知识。候选工程师需通过实操路面工程经验与规范标准熟悉度的面试考核。 边界框标注遵循DNIT的病害定义。试点标注阶段，最初计划标注8类DNIT定义的病害：开裂（裂缝）、车辙、波纹变形、推挤变形、泛油、集料松散、坑槽与修补补丁。 为提升标注可靠性，进行了两项调整： 1. 移除泛油类别：该病害常与降雨、冲洗或管道泄漏形成的积水混淆，仅通过RGB图像难以实现全自动识别，需额外辅助信息。 2. 合并类别：将波纹变形、推挤变形与车辙合并为推挤变形类，因为这三类病害的塑性变形机制相似，在DNIT标准下视觉特征重叠，且在城市道路场景中出现频率较低（高速公路场景中载重车辆荷载更常见，该类病害发生率更高）。 标注过程中出现的分歧（如边界框范围、类别分配）通过技术评审会议协商解决，最终达成统一的标注结果。 标注结果以YOLO格式导出：每张图像对应一个TXT文件，文件内容为归一化的<类别ID> <中心点x> <中心点y> <宽度> <高度>，坐标原点位于图像左上角，采用空格分隔的浮点数，无表头。 ### BR-AsPavDam数据集概况与组织 本数据集包含2167张图像，共标注3918处病害，仅供学术研究使用。 最终合并后的类别： - 裂缝（开裂） - 推挤变形（合并波纹变形、推挤变形与车辙） - 集料松散（路面集料脱落） - 坑槽 - 修补补丁（可见的养护/修补区域） 边界框分布（高分辨率原始数据集）： | 类别 | 边界框数量 | |------------|------------| | 裂缝 | 1843 | | 推挤变形 | 14 | | 集料松散 | 1097 | | 坑槽 | 125 | | 修补补丁 | 839 | | 总计 | 3918 | 初始提取的3565帧图像中，经质量控制（移除无病害或低质量图像）后保留2167张。 文件夹结构（适配目标检测模型训练）： - 数据集根目录包含images/与labels/文件夹 - images/按主要病害类别分为5个子文件夹：Fissures/、Patch/、Pothole/、Ravelling/、Shoving/（变形类） - labels/无次级文件夹，每张图像<名称>.png对应同名的<名称>.txt文件，存储YOLO格式的边界框标注 文件命名规则（以主要病害类别为基准）：采用<PredominantClass>_NNN.png与<PredominantClass>_NNN.txt的命名格式。若一张图像中出现多个频率相同的病害类别，则按照病害严重程度从高到低排序命名，优先级为：坑槽 > 修补补丁 > 推挤变形 > 裂缝 > 集料松散。 文件名仅反映图像中的主要病害类别，图像中所有出现的病害对象均会在对应的TXT文件中完整标注。 图像与标注格式： - 图像：PNG格式，分辨率为1152 × 2048像素（宽×高） - 标注：YOLO归一化格式，每个实例占一行，格式为<类别ID> <中心点x> <中心点y> <宽度> <高度> - 类别ID（0-4）：0=裂缝，1=推挤变形，2=集料松散，3=坑槽，4=修补补丁 复用与复现注意事项： - 本数据集遵循DNIT病害定义，经专家标注委员会共识审核后发布 - 采样策略（日光采集窗口、约2秒帧间隔、目标车速40 km/h、路线多样性设计）旨在平衡图像可见性与数据非冗余性 - 用户需注意城市道路场景中常见的类别不平衡问题（如推挤变形类样本量极少），并考虑采用合适的训练策略（如类别重加权、数据增强）