Results 161 to 170 of about 1,151 (212)

Magnetic Barkhausen Noise Sensor: A Comprehensive Review of Recent Advances in Non-Destructive Testing and Material Characterization. [PDF]

Sensors (Basel)
Vourna P, Falara PP, Ktena A, Hristoforou EV, Papadopoulos ND.   +4 more
europepmc   +1 more source

Lightweight Visual Detection and Dynamic Tracking for Pigeon Egg Inspection in Caged Pigeon Farming. [PDF]

Sensors (Basel)
Li Q, Cheng Y, Xi J, He Z, Ye Q, Zhu C, Kang R, Liu L.   +7 more
europepmc   +1 more source

AI enabled pulse-echo based SHM system integrated with robotic gripper for the inspection of pipelines. [PDF]

Sci Rep
Tayyab MA, Elahi H, Ali MO, Aqeel AB, Zeb A.   +4 more
europepmc   +1 more source

Preventing spread of the invasive spotted lanternfly via texture-based automated egg detection. [PDF]

Front Insect Sci
Negrete K, Butler R, Wallis E, Magnani E, Mcglynn MB, McDonald M, Alvarez NJ, Tang M.   +7 more
europepmc   +1 more source

A multi-scale adaptive framework for high-precision rail track damage detection via StarNet and bidirectional feature pyramid network. [PDF]

Sci Rep
Pang Y, Wang X, Tang Y, Lou B, Ning L, Xiong H, Teng X, Yuan Q, Bao C, Chen J, Gu G.   +10 more
europepmc   +1 more source

Development of damage detection algorithms for structural systems based on structural dynamic data

, 2005
Zumpano, Giuseppe
core  

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A Coarse-to-Fine Model for Rail Surface Defect Detection

IEEE Transactions on Instrumentation and Measurement, 2019
Computer vision systems have attracted much attention in recent years for use in detecting surface defects on rails; however, accurate and efficient recognition of possible defects remains challenging due to the variations shown by defects and also noise. This paper proposes a coarse-to-fine model (CTFM) to identify defects at different scales.
Haomin Yu, Qingyong Li, Yunqiang Tan, Jinrui Gan, Jianzhu Wang, Yangli-ao Geng, Lei Jia 0004   +6 more
openaire   +3 more sources

Rail surface defect detection based on deep learning

Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 2020
In order to ensure the safety of rail transit, detecting the flaws on the rail surface is vitally important. Instead of present manual inspections, detecting defects on rail surface by an automatic approach enables the work more efficient and safe currently.
Xiaoqing Li, Ying Zhou, Hu Chen
openaire   +3 more sources