# Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation

arXiv: https://arxiv.org/abs/2501.02414 · HTML: https://mlsanity.com/paper/2501.02414/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 118 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 30 of 42)

- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM](https://mlsanity.com/paper/2402.03246/) (2024)
- [Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection](https://mlsanity.com/paper/2203.15221/) (2022)
- [Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning](https://mlsanity.com/paper/2207.11934/) (2022)
- [NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising](https://mlsanity.com/paper/2403.20034/) (2024)
- [Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering](https://mlsanity.com/paper/2310.06238/) (2023)
- [The FacT: Taming Latent Factor Models for Explainability with Factorization Trees](https://mlsanity.com/paper/1906.02037/) (2019)
- [MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms](https://mlsanity.com/paper/2402.14154/) (2024)
- [ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding](https://mlsanity.com/paper/2408.11363/) (2024)
- [Prototypical Reward Network for Data-Efficient RLHF](https://mlsanity.com/paper/2406.06606/) (2024)
- [MMREC: LLM Based Multi-Modal Recommender System](https://mlsanity.com/paper/2408.04211/) (2024)
- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [Real-Time Pill Identification for the Visually Impaired Using Deep Learning](https://mlsanity.com/paper/2405.05983/) (2024)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [MT2ST: Adaptive Multi-Task to Single-Task Learning](https://mlsanity.com/paper/2406.18038/) (2024)
- [Data Imputation using Large Language Model to Accelerate Recommendation System](https://mlsanity.com/paper/2407.10078/) (2024)
- [XMeCap: Meme Caption Generation with Sub-Image Adaptability](https://mlsanity.com/paper/2407.17152/) (2024)
- [Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models](https://mlsanity.com/paper/2407.11033/) (2024)
- [Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach](https://mlsanity.com/paper/2409.00022/) (2024)
- [A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy](https://mlsanity.com/paper/2405.13453/) (2024)
- [AirShot: Efficient Few-Shot Detection for Autonomous Exploration](https://mlsanity.com/paper/2404.05069/) (2024)
- [An Integrated Transportation Distance Between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems](https://mlsanity.com/paper/2311.06645/) (2023)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [Minimax Optimal Q Learning with Nearest Neighbors](https://mlsanity.com/paper/2308.01490/) (2023)
- [Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects](https://mlsanity.com/paper/2310.00011/) (2023)
- [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024)
- [Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds](https://mlsanity.com/paper/1711.06540/) (2017)
- [Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior](https://mlsanity.com/paper/2404.07176/) (2024)

## Built on by (most cited first, 7 of 7)

- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [GraphRAG under Fire](https://mlsanity.com/paper/2501.14050/) (2025)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)
- [Language-Driven Coordination and Learning in Multi-Agent Simulation Environments](https://mlsanity.com/paper/2506.04251/) (2025)
- [Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving](https://mlsanity.com/paper/2501.06680/) (2025)
- [Intelligent road crack detection and analysis based on improved YOLOv8](https://mlsanity.com/paper/2504.13208/) (2025)
- [ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering](https://mlsanity.com/paper/2509.16680/) (2025)

## Abstract (arXiv)

Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement images. The contributions of this work are threefold: firstly, it proposes an economical method to acquire three-dimensional (3D) pavement texture data; secondly, it enhances 3D image processing techniques and formulates features that represent various aspects of texture; thirdly, it establishes multivariate prediction models that link these features with MTD values. Validation results demonstrate that the Gradient Boosting Tree (GBT) model achieves remarkable prediction stability and accuracy (R2 = 0.9858), and field tests indicate the superiority of the proposed method over other techniques, with relative errors below 10%. This method offers a comprehensive end-to-end solution for pavement quality evaluation, from images input to MTD predictions output.