Investigation of integrated deep learning based electrical connector anomaly detection

The automotive industry’s pivotal role underscores the urgent demand for high-precision anomaly detection in electrical connectors, driven by the surge in connector production and the corresponding increase in defective units. Electrical connectors, integral to signal and energy transmission, are wi...

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Bibliographic Details

Main Authors:	Ahmad Shahrizan, Abdul Ghani, Ee, Chern Ting, Nurul Haziyani, Aris, Azri Hizami, Abd Rasid, Mohd Yazid, Abu, Mohd Fauzi, Abu Hassan, Xin, Jin, Nagata, Fusaomi
Format:	Conference or Workshop Item
Language:	en
Published:	IEEE 2026
Subjects:	TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TR Photography
Online Access:	https://umpir.ump.edu.my/id/eprint/47351/1/Investigation_of_Integrated_Deep_Learning_Based_Electrical_Connector_Anomaly_Detection.pdf https://umpir.ump.edu.my/id/eprint/47351/ https://doi.org/10.1109/SCOReD68498.2025.11399132
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