Kualitas Dataset dan Strategi YOLO untuk Deteksi Cacat Jahitan Berukuran Kecil: Tinjauan Literatur Sistematis menuju Inspeksi Bordir Real-Time
DOI:
https://doi.org/10.55826/jtmit.v5i3.2002Keywords:
anotasi dataset, cacat jahitan, inspeksi bordir, kualitas dataset, small-object detection, YOLOAbstract
Inspeksi cacat bordir berbasis computer vision memerlukan dataset yang konsisten dan model yang mampu mendeteksi pola kecil, tipis, memanjang, serta berkontras rendah. Penelitian ini menyintesis praktik kualitas dataset dan strategi You Only Look Once (YOLO) untuk deteksi cacat tekstil sebagai dasar pengembangan inspeksi bordir real-time. Tinjauan dilakukan berdasarkan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Pencarian Scopus menghasilkan 284 dokumen dan menyisakan 82 dokumen setelah penyaringan tahun, jenis dokumen, bahasa, dan akses terbuka. Sebanyak 61 artikel dinilai berdasarkan naskah lengkap, dan 34 studi primer disertakan dalam sintesis, sedangkan sumber lain yang relevan digunakan sebagai bukti pendukung. Data diekstraksi berdasarkan sumber dataset, akuisisi, anotasi, pembagian data, augmentasi, arsitektur YOLO, strategi small-object detection, dan kinerja komputasi. Hasil menunjukkan dominasi pendekatan model-centric berupa attention mechanism, multi-scale feature fusion, lightweight architecture, loss function optimization, operator konvolusi khusus, dan small-object detection head. Sebaliknya, definisi operasional kelas, pedoman anotasi bounding box, quality control label, metadata akuisisi, dan pencegahan data leakage masih terbatas. Kerangka terintegrasi diusulkan untuk menghubungkan definisi cacat, akuisisi, kurasi, anotasi, pengembangan YOLO, serta validasi akurasi, kecepatan, dan robustness sebagai landasan penelitian cacat bordir pada Usaha Mikro, Kecil, dan Menengah (UMKM).
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