International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Steel Plate Defect Detection Using Machine Learning and Deep Learning: A Review

Komal Raju Nimsarkar, Supriya Sawwashere, Shrikant V. Sonekar, Ashutosh Lanjewar, Mirza Moiz Baig

Abstract :

Steel plate defect detection is a critical quality control process in manufacturing industries to ensure product reliability and safety. Recent advancements in machine learning (ML) and deep learning (DL) have significantly improved the accuracy and automation of defect detection systems. This paper reviews the current approaches and techniques used in steel plate defect detection, highlighting traditional machine learning models such as Random Forest and XGBoost, as well as deep learning architectures like Convolutional Neural Networks (CNNs) and MobileNetV2. We compare different methods, discuss their strengths and limitations, and outline future research directions. This review aims to provide insights into how intelligent systems can be further enhanced for real-world industrial applications.

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