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Fire and Smoke Object Detection Using Mask R-CNN
AbstractPenelitian ini berfokus pada penggunaan teknologi computer vision, khususnya metode Mask R-CNN, dalam deteksi api dan asap pada kasus kebakaran hutan. Kebakaran hutan adalah masalah lingkungan yang serius, di mana metode deteksi tradisional sering terbatas oleh jangkauan visual dan kesalahan subjektif. Kami mengeksplorasi potensi teknologi computer vision sebagai solusi yang lebih efisien dan akurat. Dataset yang digunakan sebanyak 3465 gambar yang telah dianotasi dengan menggunakan roboflow. Jumlah dataset yang digunakan pada data training 2964 gambar, data validasi 854 gambar dan data testing 427 gambar. Model deteksi api dan asap menggunakan mask rcnn dengan menggunakan parameter learning rate 0.0025, image per batch 2 dan max iteration 10000. Adapun hasil yang diperolah pada average precision = 0.38 dan average recall = 0.29 |
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References
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