Multi-Scale Convolutional Networks untuk Pengenalan Rambu Lalu Lintas di Indonesia
DOI:
https://doi.org/10.32736/sisfokom.v11i3.1452Keywords:
multi-scale convolutional networks, pengenalan rambu lalu lintas, rambu lalu lintasAbstract
Teknologi pengenalan rambu lalu lintas yang sering disebut dengan traffic sign recognition (TSR), digunakan untuk mengenali rambu lalu lintas melalui pemanfaatan pengolahan citra. TSR sendiri dapat diaplikasikan pada sistem pembantu pengemudi, sistem pembantu pengemudi tingkat lanjut, sistem mengemudi otonom, keamanan jalan raya, pemahaman suasana perkotaan, dan pemantauan rambu untuk kepentingan perawatan. Perbaruan dari pengenalan rambu lalu lintas di Indonesia menggunakan multi-scale convolutional neural network (CNN) telah disajikan pada artikel ini. Dataset yang digunakan pada penelitian ini berjumlah 2050 data citra rambu lalu lintas yang dikelompokkan kedalam 10 kelas. Model CNN terdiri dari tiga lapisan konvolusi berukuran 3x3, tiga lapisan penggabungan (Maxpool) berukuran 2x2 dan satu lapisan fully-connected yang memanfaatkan fungsi aktivasi Softmax. Jumlah filter yang digunakan pada setiap lapisan konvolusi adalah 32. Algoritma pelatihan yang digunakan yaitu Stochastic gradient descent (SGD). Dengan menggunakan 1750 data citra latih, nilai epoch 20, dan laju pelatihan 0,005, nilai galat dan nilai akurasi yang didapatkan pada tahap pelatihan adalah masing-masing 0,0026 dan 100%. Sedangkan pada tahap pengujian, dengan 300 data citra uji, model CNN mampu memperoleh nilai galat 0,017 dan nilai akurasi mencapai 99,67%.References
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