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Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application
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Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application
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Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application
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Journal Article
Design of Real-Time Object Detection in Mobile Robot for Volcano Monitoring Application
2022
Overview
Indonesia is one of the countries located at the ring of fire which should be monitored to predict the eruption earlier and set the risk zones around with no human involvement especially while eruption taking place. Therefore, in this research, it is used a 4 wheeled mobile robot called PRAWIRA for this purpose. The robot should have the ability to avoid the obstacles in front of it in this area. It has been designed a real-time object detection system for volcano monitoring application using deep learning from the YOLOv5s model for 4 objects (trees, persons, stones, and stairs). It was used 484 images for the dataset after the pre-train process was conducted with several steps: object identification; dataset downloading (Google Chrome Extension and Open Images v6); image labeling (LabeImg); augmentation process (flip, blur, and rotation); and data training for varies epochs and batches by Jupyter Notebook GPU. The preliminary result for this research was presented in the mean average precision (mAP) of YOLOv5s (the smallest version). The first variation (batch = 16, epochs = 100) resulted in mAP_0.5 = 17.9% and mAP_0.5:0.95 = 7.27% with 0.262 hours of training time. The second (batch = 16, epochs = 500) resulted in mAP_0.5 = 25.7% and mAP_0.5:0.95 = 12.3% with 1.296 hours of training time, while the third (batch = 80, epochs = 100) resulted in mAP_0.5 = 17.7% and mAP_0.5:0.95 = 5.63% with 0.232 hours of training time. Furthermore, the last variation (batch = 80, epochs = 500) resulted in mAP_0.5 = 19.5% and mAP_0.5:0.95 = 8.92% with 1.085 hours of training time. Therefore, the second variation is the best result for the model with 14.8 MB of size. Moreover, interfaces for the best model were displayed to show the result of the training.
