{"total":33,"rows":[{"id":"2208.13054","title":"CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks","created":"2022-08-27","cited_by_count":22,"area":null},{"id":"2204.13590","title":"Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms","created":"2022-04-28","cited_by_count":19,"area":null},{"id":"2510.03797","title":"Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach","created":"2025-10-04","cited_by_count":9,"area":null},{"id":"2408.12364","title":"SAM-SP: Self-Prompting Makes SAM Great Again","created":"2024-08-22","cited_by_count":8,"area":null},{"id":"2109.00456","title":"Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding","created":"2021-09-01","cited_by_count":6,"area":null},{"id":"2401.15647","title":"UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration","created":"2024-01-28","cited_by_count":6,"area":null},{"id":"2202.03714","title":"What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification","created":"2022-02-08","cited_by_count":4,"area":null},{"id":"2309.11267","title":"From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring","created":"2023-09-20","cited_by_count":4,"area":null},{"id":"2109.05226","title":"Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads","created":"2021-09-11","cited_by_count":3,"area":null},{"id":"2112.13082","title":"Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection","created":"2021-12-24","cited_by_count":3,"area":null},{"id":"2308.00828","title":"Deep Learning Approaches in Pavement Distress Identification: A Review","created":"2023-08-01","cited_by_count":3,"area":null},{"id":"2103.06467","title":"Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines","created":"2021-03-11","cited_by_count":2,"area":null},{"id":"2104.10511","title":"Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection","created":"2021-04-21","cited_by_count":2,"area":null},{"id":"2203.02355","title":"Computer-Aided Road Inspection: Systems and Algorithms","created":"2022-03-04","cited_by_count":2,"area":null},{"id":"2209.02190","title":"A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images","created":"2022-09-06","cited_by_count":2,"area":null},{"id":"2110.12700","title":"An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018","created":"2021-10-25","cited_by_count":1,"area":null},{"id":"2111.06155","title":"A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network","created":"2021-11-11","cited_by_count":1,"area":null},{"id":"2202.01421","title":"Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones","created":"2022-02-03","cited_by_count":1,"area":null},{"id":"2206.06743","title":"Weakly-Supervised Crack Detection","created":"2022-06-14","cited_by_count":1,"area":null},{"id":"2212.01790","title":"Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition","created":"2022-12-04","cited_by_count":1,"area":null},{"id":"2402.04064","title":"Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2503.18082","title":"Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2108.04235","title":"Deep Transfer Learning for Identifications of Slope Surface Cracks","created":"2021-08-08","cited_by_count":0,"area":null},{"id":"2109.05493","title":"LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection","created":"2021-09-12","cited_by_count":0,"area":null},{"id":"2202.13285","title":"A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management","created":"2022-02-27","cited_by_count":0,"area":null},{"id":"2205.01783","title":"A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure","created":"2022-04-17","cited_by_count":0,"area":null},{"id":"2206.01498","title":"YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection","created":"2022-06-03","cited_by_count":0,"area":null},{"id":"2305.05132","title":"Dual flow fusion model for concrete surface crack segmentation","created":"2023-05-09","cited_by_count":0,"area":null},{"id":"2307.04298","title":"Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection","created":"2023-07-10","cited_by_count":0,"area":null},{"id":"2408.15513","title":"Continual-learning-based framework for structural damage recognition","created":"2024-08-28","cited_by_count":0,"area":null},{"id":"2510.17566","title":"WP-CrackNet: A Collaborative Adversarial Learning Framework for End-to-End Weakly-Supervised Road Crack Detection","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2511.20541","title":"Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2606.12958","title":"YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection","created":"2026-06-11","cited_by_count":0,"area":null}],"next_cursor":null}