Perbandingan Modul Atensi Coordinate Attention (CA) dan Convolutional Block Attention Module (CBAM) pada Arsitektur YOLOv8n untuk Deteksi Retak Jembatan
DOI:
https://doi.org/10.29407/y5z37q53Abstract
Pemeliharaan jembatan membutuhkan inspeksi retak rambut guna mencegah kegagalan struktural. Inspeksi manual memakan waktu dan sulit mengidentifikasi retakan tipis pada permukaan beton yang bising (noisy background). Penelitian ini bertujuan mengembangkan sistem deteksi otomatis dengan membandingkan efektivitas YOLOv8n standar terhadap model modifikasi bermekanisme atensi: Coordinate Attention (CA) dan Convolutional Block Attention Module (CBAM). Manfaat penelitian ini adalah menyediakan landasan sistem peringatan dini yang akurat, real-time, dan hemat biaya komputasi. Temuan ini berdampak pada efisiensi pengawasan karena membuktikan fitur dasar YOLOv8n sudah optimal untuk retakan tipis tanpa perlu arsitektur tambahan. Evaluasi dilakukan menggunakan dataset citra retak jembatan dengan mengukur metrik Precision, Recall, dan mAP. Hasil eksperimen menunjukkan model baseline YOLOv8n mencapai kinerja tertinggi dengan Precision 83%, Recall 72%, mAP@50 78%, dan mAP@50-95 49%. Sebaliknya, penambahan modul atensi menunjukkan sedikit penurunan performa; model CBAM menghasilkan mAP@50 sebesar 77% dan model CA sebesar 76%.
Keywords:
crack detection, YOLOv8n, Coordinate Attention, CBAM, infrastructure, bridge##plugins.themes.default.displayStats.downloads##
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