Results 201 to 210 of about 5,176,577 (228)

Modal Evaluation Network via Knowledge Distillation for No-Service Rail Surface Defect Detection

IEEE transactions on circuits and systems for video technology (Print)
Deep learning techniques have largely solved the problem of rail surface defect detection (SDD), however, two aspects have yet to be addressed. In most existing approaches, two red–green–blue and depth (RGB-D) streams are indiscriminately fused across ...
Wu-Jie Zhou, Jian-Kang Hong, Wei-Qing Yan, Qiu-Ping Jiang   +3 more
semanticscholar   +1 more source

Automatic Rail Surface Defect Inspection Using the Pixelwise Semantic Segmentation Model

IEEE Sensors Journal, 2023
Rail surface defect (RSD) is an important railroad track quality indicator and impacts the overall track safety and ride quality. Railroads need to inspect and evaluate RSD conditions regularly to make proper maintenance plan for both freight and ...
F. Guo, Yu Qian, Huayang Yu
semanticscholar   +1 more source

DSANet-KD: Dual Semantic Approximation Network via Knowledge Distillation for Rail Surface Defect Detection

IEEE transactions on intelligent transportation systems (Print)
Owing to the development of convolutional neural networks (CNNs), the detection of defects on rail surfaces has significantly improved. Although existing methods achieve good results, they incur huge computational and parameter costs associated with CNNs.
Wu-Jie Zhou, Jian-Kang Hong, Xiaoxiao Ran, Wei-Qing Yan, Qiu-Ping Jiang   +4 more
semanticscholar   +1 more source

Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect Inspection

IEEE transactions on intelligent transportation systems (Print)
Visual-based methods for rail surface defect inspection (RSDI) effectively improve the limitations of manual inspection, as they can intuitively display the locations and segmented areas of sensitive defects.
Jie Wang, Guoqiang Li, Guanwen Qiu, Gan Ma, Jinwen Xi, Na-Na Yu   +5 more
semanticscholar   +1 more source

Lightweight rail surface defect detection algorithm based on an improved YOLOv8

Measurement: Journal of the International Measurement Confederation
Canyang Xu, Ying-Ying Liao, Yong-Qiang Liu, Run-Liang Tian, Tao Guo   +4 more
exaly   +2 more sources

Region and Edge-Aware Network for Rail Surface Defect Segmentation

IEEE Transactions on Instrumentation and Measurement
Rail surface defect segmentation can provide a reliable basis for the severity evaluation of rail diseases. Deep learning technology has been widely applied to segment rail surface defects due to its powerful feature representation ability. However, most
Yuan Qiu, Hongli Liu, Jianwei Liu, Bo Shi, Yan-Fu Li   +4 more
semanticscholar   +1 more source

Lightweight Scope Integration Network for Rail Surface Defect Detection

IEEE Transactions on Big Data
Rail-surface defect detection (RSDD) is a key technology for ensuring the safety and efficiency of railroad transportation. Existing models enhance the robustness in complex scenarios by using complementary information from visible light (RGB) image and ...
Wu-Jie Zhou, Yue Wu, Fangfang Qiang, Wei-Qing Yan   +3 more
semanticscholar   +1 more source

Method for rail surface defect detection based on neural network architecture search

Measurement science and technology
This study addresses the inherent limitations of implementing neural network architecture search algorithms for rail surface defect detection, including low search efficiency and the oversight of edge features on the rail surface.
Yongzhi Min, Qi Jing, Yaxing Li
semanticscholar   +1 more source

Normalized Cyclic Loop Network for Rail Surface Defect Detection Using Knowledge Distillation

IEEE transactions on intelligent transportation systems (Print)
In recent years, the application of computer vision for detecting rail defects has shown promising results. However, as the accuracy of the models improves, they become more complex with a large number of parameters, making it challenging to use them in ...
Xinyu Sun, Wu-Jie Zhou, Xiao-Hong Qian
semanticscholar   +1 more source

Research on rail surface defect detection algorithm based on improved yolov8

2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)
In this paper, a rail surface defect detection and classification algorithm based on the improved YOLOv8 is proposed, aiming to improve the accuracy and efficiency of defect detection in the rail maintenance process.
Bu Tong, Tao He
semanticscholar   +1 more source