STEEL SURFACE DEFECT INSPECTION SYSTEM BY USING DEEP LEARNING SEGMENTATION MODEL UNET - Dalam bentuk buku karya ilmiah
MARADEN NAEK NAINGGOLAN
Informasi Umum
Kode
23.04.7582
Klasifikasi
621.38 2 - Signal processing, Information theory
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Classification, Deep Learning,
Dilihat
17 kali
Informasi Lainnya
Abstraksi
<p>Steel is the most essential material in the world of engineering and construction. Modern steelmaking relies on computer vision technologies, like optical cameras, to monitor the production and manufacturing processes, which helps companies improve product quality.</p>
<p>Traditional object detection methods are based on handcrafted features, which have problems like needing an excellent precision rate, flexibility, etc. Hence, the technique used for this paper is segmentation methods in defect detection. It classifies the object and its defects and then is reviewed with categories of characteristics, strengths, and shortcomings.</p>
<p>The simulation and analysis results in this final thesis reveal that the model can produce good results, with an Intersection Over Union (IOU) of 0.96 in defect inspection on the steel surface. Keywords: Defect inspection; deep learning; image processing; image segmentation</p>
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | MARADEN NAEK NAINGGOLAN |
| Jenis | Perorangan |
| Penyunting | Nur Ibrahim, Fityanul Akhyar |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Teknik Telekomunikasi (International Class) |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
