Papers Road Damage Detection
“Road Damage Detection” 태그가 달린 논문 33편 · 필터 해제
YOLO26-RD: An End-to-End Road Damage Detection Network With Learnable Contrast Enhancement and Edge-Guided Downsampling
Pavement distress detectors are conventionally specialised for small objects, typically by adding a stride-4 detection head and replacing strided convolution with space-to-depth downsampling. This paper tests that premis…
Road Damage DetectionIntelligent Road Condition Monitoring using 3D In-Air SONAR Sensing
In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material classification and Road damage detection an…
Road Damage DetectionComparative Analysis of Custom CNN Architectures versus Pre-trained Models and Transfer Learning: A Study on Five Bangladesh Datasets
This study presents a comprehensive comparative analysis of custom-built Convolutional Neural Networks (CNNs) against popular pre-trained architectures (ResNet-18 and VGG-16) using both feature extraction and transfer le…
Road Damage DetectionImage ClassificationTransfer LearningSelf-Supervised Visual Prompting for Cross-Domain Road Damage Detection
The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, w…
Domain GeneralizationRoad Damage DetectionStripRFNet: A Strip Receptive Field and Shape-Aware Network for Road Damage Detection
Well-maintained road networks are crucial for achieving Sustainable Development Goal (SDG) 11. Road surface damage not only threatens traffic safety but also hinders sustainable urban development. Accurate detection, how…
Road Damage DetectionObject DetectionYOLO-ROC: A High-Precision and Ultra-Lightweight Model for Real-Time Road Damage Detection
Road damage detection is a critical task for ensuring traffic safety and maintaining infrastructure integrity. While deep learning-based detection methods are now widely adopted, they still face two core challenges: firs…
Road Damage DetectionTD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
Object detection has witnessed remarkable advancements over the past decade, largely driven by breakthroughs in deep learning and the proliferation of large scale datasets. However, the domain of road damage detection re…
object-detectionObject DetectionReal-Time Object DetectionRoad Damage DetectionRDD4D: 4D Attention-Guided Road Damage Detection And Classification
Road damage detection and assessment are crucial components of infrastructure maintenance. However, current methods often struggle with detecting multiple types of road damage in a single image, particularly at varying s…
Road Damage DetectionOptimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10
Maintaining roadway infrastructure is essential for ensuring a safe, efficient, and sustainable transportation system. However, manual data collection for detecting road damage is time-consuming, labor-intensive, and pos…
Road Damage DetectionReal-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example
Unmanned Aerial Vehicle (UAV)-based Road Damage Detection (RDD) is important for daily maintenance and safety in cities, especially in terms of significantly reducing labor costs. However, current UAV-based RDD research …
2D Object Detectionobject-detectionObject DetectionReal-Time Object Detection+1DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
Current road damage detection methods, relying on manual inspections or sensor-mounted vehicles, are inefficient, limited in coverage, and often inaccurate, especially for minor damages, leading to delays and safety haza…
Road Damage DetectionCut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detection
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 …
Data AugmentationObjectobject-detectionObject Detection+1Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection
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. P…
Road Damage DetectionTexture SynthesisInconSeg: Residual-Guided Fusion With Inconsistent Multi-Modal Data for Negative and Positive Road Obstacles Segmentation
Segmentation of road obstacles, including negative and positive obstacles, is critical to the safe navigation of autonomous vehicles. Recent methods have witnessed an increasing interest in using multi-modal data fusion …
Autonomous VehiclesDecoderRoad Damage DetectionSegmentationCrowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The Big Data Cup challenges involve a releas…
Ensemble LearningRoad Damage DetectionRoad Damages Detection and Classification with YOLOv7
Maintaining the roadway infrastructure is one of the essential factors in enabling a safe, economic, and sustainable transportation system. Manual roadway damage data collection is laborious and unsafe for humans to perf…
ClassificationDeep LearningRoad Damage DetectionAI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage
Road infrastructure maintenance inspection is typically a labor-intensive and critical task to ensure the safety of all road users. Existing state-of-the-art techniques in Artificial Intelligence (AI) for object detectio…
Few-Shot Learningobject-detectionObject DetectionOut-of-Distribution Detection+1Road Rutting Detection using Deep Learning on Images
Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are bein…
Deep LearningObjectobject-detectionObject Detection+2RDD2022: A multi-national image dataset for automatic Road Damage Detection
The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated w…
object-detectionObject DetectionRoad Damage DetectionComputer-Aided Road Inspection: Systems and Algorithms
Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and …
Road Damage Detection