Deteksi Cacat Produk Manufaktur Menggunakan Convolutional Neural Network pada Citra Industri

Ahmad Fauzan; Putri Rahmadani

doi:10.5555/audhighasu.v1i2.7

Manufacturing Defect Detection Using Convolutional Neural Networks on Industrial Images

Authors

Ahmad Fauzan Audhighasu University Author
Putri Rahmadani Sepuluh Nopember Institute of Technology Author

DOI:

https://doi.org/10.5555/audhighasu.v1i2.7

Keywords:

computer vision, CNN, defect detection, EfficientNet, industry 4.0

Abstract

Manual visual inspection is prone to human error. This study applies EfficientNet-B3 to detect surface defects on electronic products. A private dataset of 8,200 images covers six defect classes. The model achieves 0.91 mAP and runs on a Jetson Nano edge device at 22 FPS.

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Author Biographies

Ahmad Fauzan, Audhighasu University

Peneliti machine learning dan visi komputer.
Putri Rahmadani, Sepuluh Nopember Institute of Technology

Dosen teknik informatika.