Review on Rail Damage Detection Technologies for High-Speed Trains by Yu Wang, Bingrong Miao, Ying Zhang, Zhong Huang, Songyuan Xu

“…In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. …”
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A Deep Learning-Based Algorithm for Ceramic Product Defect Detection by Junxiang Diao, Hua Wei, Yawei Zhou, Zhihua Diao

“…In crack detection, the average Edge Localization Error (ELE) is reduced by 25%, the Edge Connectivity Rate (ECR) is increased by 15%, the Weak Edge Responsiveness (WER) is improved by 17%, and the frame rate reaches 40 frames per second (f/s), meeting real-time detection requirements. …”
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A method for determining fracturing radius of blasting holes based on SF6 gas tracing by Dongxu JIA

“…Aiming at this engineering problem, based on the unique chemical inertness, strong diffusion characteristics and highly sensitive detection advantages of sulfur hexafluoride (SF6) gas, an in-situ dynamic detection method based on the tracer principle of SF6 gas is proposed, and a multi-module coupled crack radius detection system is developed. …”
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Thermal damage detection of EAST internal component based on machine learning by Zhongfang Guan, Bin Zhang, Jian Liu, Jinping Qian, Xianzu Gong, Runze Chen, Zuhao Wang, Binfu Gao, Yutong Guo, Chuannan Xuan, Cong Cao, Tianqi Jia, Pan Li, Wenbin Liu, Wei Wang, Yunchan Hu, Yifan He, Kangjia Yang, Wenyi Lu, Chunyu He, the EAST team

“…Therefore, we need efficient detection methods to monitor thermal events in real-time. …”
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Determination of Lactoferrin Using High-Frequency Piezoelectric Quartz Aptamer Biosensor Based on Molecular Bond Rupture by Haizhi Wu, Shihui Si, Zheng Li, Jiayou Su, Shangguan Jia, Hao He, Chengcheng Peng, Tongqiang Cheng, Qian Wu

“…In this study, an aptamer biosensor for detecting lactoferrin (LF) was developed using piezoelectric quartz-induced bond rupture sensing technology. …”
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Experimental study on seepage characteristics model of tunnel lining based on infrared imaging by Zhijian Wu, Hua Wu, Yichen Peng, Renjie Song, Yimin Wu, Haiping Wu, Guangzheng Zhuang

“…The results show that: (1) Geometric characteristics of seepage areas for various crack types were systematically summarized, where crack patterns and their positions determine the geometric features of seepage regions; (2) Seepage areas of all crack types increase with flow rate, and different crack morphologies exhibit distinct susceptibility to flow rate at varied positions; (3) The centroid distance curve of the seepage core area derived from isothermal maps enables rapid identification of crack morphologies and their positions within the tunnel by analyzing curve patterns. …”
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Rail hidden damage detection based on the modal curvature differences method by Weirong Li, Yan Liu, Xianpu Yuan, Qiutong Li, Guojian Zhou

“…Setting cracks at rail bottom and rail jaw, the detection errors were below 5% at a 6 mm crack depth and lower than 3% at a 10 mm crack depth. …”
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Numerical and Experimental Investigation of Gas Turbine Rotor for Early Fault Detection by Muhammad Akhtar, Waqar Muhammad Ashraf, Nasir Hayat, Ghulam Moeen Uddin, Fahid Riaz

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Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection by Zhaohui Wu, Runjing Zhao, Xingliang Sun, Zhaojia Li

“…Current methods, integrating Single Shot Detector with MobileNet and Faster R-CNN, have successfully detected large potholes and long, deep cracks. However, detecting smaller, shallower cracks and minor potholes remains a challenge. …”
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Rice Appearance Quality Detection Technology Based on Muti-demensional Features by XING Jian, LUO Jia-shun

“…After many tests, the results show that the detection accuracy of the system for broken rice rate of random rice samples is 97. 01 % , rice seed detection accuracy is 97. 60%, crack detection accuracy is 98. 22%, which is better than the traditional manual detection method. …”
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Damage Detection and Segmentation in Disaster Environments Using Combined YOLO and Deeplab by So-Hyeon Jo, Joo Woo, Chang Ho Kang, Sun Young Kim

“…In conventional approaches, close-up images have been used to detect and segment damage images such as cracks. In this study, the method of using a deep learning model is proposed for the rapid determination and analysis of multiple damage types, such as cracks and concrete rubble, in disaster sites. …”
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Detection of distress in rural road pavements: an analysis of UAV altitude variations by L. Inzerillo, F. Acuto, A. Pisciotta, K. Mantalovas, G. Di Mino

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Detection and classification of Shiitake mushroom fruiting bodies based on Mamba YOLO by Kangkang Qi, Zhen Yang, Yangyang Fan, Hualu Song, Zhichao Liang, Shuai Wang, Fengyun Wang

“…Overall, the lightweight design, precise detection accuracy, and efficient detection speed of mamba-YOLO provide robust technical support for shiitake mushroom harvesting robots.…”
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High Sensitive Methods for Health Monitoring of Compressor Blades and Fatigue Detection by Mirosław Witoś

“…The diagnostic and research aspects of compressor blade fatigue detection have been elaborated in the paper. The real maintenance and overhaul problems and characteristic of different modes of metal blade fatigue (LCF, HCF, and VHCF) have been presented. …”
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Bridge facility health detection method based on DATA random subspace by Xiaoyan Shen

“…Aiming at the shortcomings of traditional bridge health detection methods in terms of modal recognition accuracy and noise resistance, the study proposes an improved bridge health detection method. …”
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Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects by Lesia Dubchak, Anatoliy Sachenko, Yevgeniy Bodyanskiy, Carsten Wolff, Nadiia Vasylkiv, Ruslan Brukhanskyi, Volodymyr Kochan

“…Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. …”
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Fault detection in beam structure using adaptive immune based approach by Sasmita Sahu, Shakti P. Jena

“…Recently with the application of machine learning approaches and the soft computing, the damage can be detected easily. In this methodology, Clonal Section Algorithm (CSA) has been applied to find out the faults (crack locations and depth) in the structure initially. …”
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Radiographic detection of welding defects based on neural ordinary differential equations by Wenyong WU, Hongyu CHENG, Qican ZHANG

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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. …”
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Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules by Khalfalla Awedat, Masoud Alajmi, Mustafa Elfituri

“…The U-Net-ASPP_Cent model is particularly effective for central anomaly detection, while the U-Net-ASPP_Diag model excels at identifying directional faults such as cracks. …”
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