Pembelajaran Kolaboratif Berdasarkan Two-Branch Neural Network dan YOLOv5 Untuk Deteksi Objek Pada Kendaraan Otonom
Abstract
Abstrak—Seiring dengan kemajuan teknologi dan otomatisasi, perkembangan pada Autonomous Vehicle (AV) meningkat secara signifikan. Object detection memegang peranan penting pada teknologi AV. Pada penerapannya, kondisi cuaca yang buruk mengakibatkan terjadinya penurunan performa sistem dalam mendeteksi objek terutama ketika cuaca berkabut. Tugas Akhir ini menganalisis konfigurasi dari pembelajaran kolaboratif an- tara algoritma dehazing dan object detection untuk meningkatkan kinerja sistem AV dalam mendeteksi objek di kondisi cuaca berkabut. Algoritma dehazing yang digunakan adalah Two- Branch Neural Network, sedangkan algoritma object detection yang digunakan adalah YOLOv5. Pada YOLOv5 dilakukan optimasi dengan hyperparameter tuning untuk mendapatkan nilai pengukuran terbaik. Hasil penelitian menunjukkan bahwa model pembelajaran kolaboratif memiliki mAP yang lebih tinggi dari model YOLOv5 orisinal, dengan nilai 71,5%. Di sisi lain, konfigurasi hyperparameter terbaik didapatkan pada nilai learn- ing rate 0,00334; batch size 32; dan lainnya didapatkan dari hyperparameter VOC. Hal ini meningkatkan mAP dari 71,5% ke 74,8%.
Kata kunci—AV, YOLOv5, two-branch neural network, object detection, image dehazing, hyperparameter
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