Nov 1, 2023 · This paper proposes a dual-task integration algorithm for concrete crack object detection and semantic segmentation based on the latest single-stage neural networks YOLOv5 (version 6.2) model (Jocher, 2022). The method proposed in this paper provides a complete approach from real-time detection and semantic segmentation of cracks to ...

Mar 12, 2025 · This review paper aims to provide a comprehensive overview of the different approaches used for crack detection and segmentation through ML and DL methods. The paper begins by discussing the fundamentals of crack detection …

Jul 13, 2020 · In this paper, the authors organize and provide up-to-date information on on ML-based crack detection algorithms for researchers to more efficiently seek potential focus and direction. The authors first reviewed 68 ML-based crack detection methods to identify the current trend of development, pixel-level crack segmentation.

Mar 3, 2021 · Artificial Intelligence takes the lead, and more specifically, Deep Learning by training our machines to be able to replace the human in the tedious task of detecting cracks on photos of structures. There are three levels of crack detection from photos: · The image is divided into patches and each patch is assigned a crack or non-crack label.

Jun 24, 2024 · In this research, we put forth a method of transfer learning-based deep convolutional neural networks (DCNN) with the pre-trained weights as a classifier and feature extractor, which exhibits a...

Jan 27, 2022 · In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented.

Deep learning-based method for identifying and localizing railway track cracks, a major concern in railway maintenance. The method uses two advanced Deep learning models, YOLOv5 for object identification and Efficient Net for classification tasks.

Oct 25, 2023 · This review emphasizes two key approaches for crack detection: deep learning and traditional computer vision, with a focus on data-driven aspects that rely primarily on data from training datasets to detect and quantify the severity level of the crack.

(CNN) model to automate the detection and classification of cracks in train tracks, streamlining the inspection process. The focus is on improving both the accuracy of crack detection and the computational efficiency, making the system suitable for real-time use in railway monitoring.

Our objective is to accurately and eficiently detect and identify cracks and gaps of various sizes in railroad tracks from multiple camera angles using object detection machine learning models.