Evolusi Corner Detection dalam Computer Vision: Analisis Komprehensif Metode Klasik dan Deep Learning
DOI:
https://doi.org/10.51903/elkom.v19i1.3907Keywords:
Corner Detection, Computer Vision, Image Processing, Deep Learning, Systematic Literature ReviewAbstract
Corner detection merupakan salah satu teknik penting dalam computer vision yang digunakan untuk mendeteksi titik-titik penting pada citra dan mendukung berbagai aplikasi seperti object detection, image registration, image matching, dan medical imaging. Seiring berkembangnya teknologi, berbagai metode corner detection telah dikembangkan mulai dari pendekatan konvensional seperti Harris Corner Detector hingga metode berbasis deep learning. Penelitian ini bertujuan untuk menganalisis perkembangan metode corner detection, mengevaluasi kinerja metode yang digunakan, mengidentifikasi dataset yang digunakan, membandingkan kelebihan dan kekurangan metode konvensional dan deep learning, serta mengidentifikasi tren penelitian terkini. Penelitian dilakukan menggunakan metode Systematic Literature Review (SLR) dengan pendekatan Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Proses pencarian literatur menghasilkan 241 artikel dari berbagai database ilmiah. Setelah melalui proses screening dan evaluasi kelayakan, diperoleh 23 artikel yang digunakan sebagai sumber utama sintesis penelitian dan 118 artikel relevan yang digunakan untuk analisis tren dan pemetaan penelitian. Hasil penelitian menunjukkan bahwa metode berbasis Convolutional Neural Network (CNN) dan pendekatan hybrid mendominasi penelitian terkini karena mampu memberikan akurasi yang lebih tinggi dibandingkan metode konvensional. Selain itu, penerapan corner detection semakin luas pada bidang remote sensing, UAV, dan medical imaging. Temuan ini menunjukkan bahwa penelitian corner detection berkembang menuju pendekatan yang lebih adaptif, akurat, dan robust terhadap berbagai kondisi citra.
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