Automated defect classification and localization in sewer pipelines using hybrid ResNet50–Swin transformer and modified YOLOv8 on CCTV inspection images

Nature·Nov 25

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring the use of various machine learning models, including hybrid ResNet50-Swin transformer and modified YOLOv8, for automated defect classification and localization in sewer pipelines. Other studies are investigating the application of similar technologies, such as YOLOv5 and YOLOv8, for defect detection in different contexts, including pipelines, bridges, and fabric surfaces. The development of robotic systems, including crawler robots and re-configurable robots, is also being researched for pipeline inspection and navigation.

Full coverage

Automated Aging Building Defect Recognition and Analysis Using Transformers

ASCE Library·Mar 18

Design of an in-pipe inspection robotic system (IPIRS) with YOLOv8–LSTM integration for real-time in-pipe navigation

Nature·Mar 22

RDLK-YOLO enhanced pipeline defect detection in uneven illumination

Nature·Dec 11

Heat exchanger pipe orifice recognition using an improved YOLOv5 model integrated with dual attention mechanisms

Nature·Jan 10

Research on fabric surface defect detection algorithm based on improved Yolo_v4

Nature·Mar 6

BD-YOLOv8s: enhancing bridge defect detection with multidimensional attention and precision reconstruction

Nature·Aug 12

A modular, multi-sensor crawler robot for adaptive pipeline inspection: design and experimental validation

Nature·Jan 8

Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

Nature·Nov 17

View original report at Nature ↗

Fetching the latest headlines…