DETECTION OF MOLLO WOVEN FABRIC MOTIFS FROM EAST NUSA TENGGARA USING A SIMILARITY AND OBJECT DETECTION APPROACH
According to the Big Indonesian Dictionary, woven cloth is a work of art that uses thread in the manufacturing process. Woven fabric is one of Indonesia's cultural heritages originating from various regions, one of which is Mollo. Mollo is one of the regions in South Central Timor. Mollo wov...
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/78146 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | According to the Big Indonesian Dictionary, woven cloth is a work of art that uses
thread in the manufacturing process. Woven fabric is one of Indonesia's cultural
heritages originating from various regions, one of which is Mollo. Mollo is one of
the regions in South Central Timor. Mollo woven fabric can be differentiated
based on two factors, namely the manufacturing technique and the motif of the
woven fabric itself. Today machine learning has penetrated various areas of
human life, one of which is culture and art. This can be used to identify woven
fabric motifs. The problem is that datasets are still difficult to obtain, apart from
that, Mollo woven cloth motifs also increase over time due to religious and
cultural processes, so it is possible that in one woven cloth there are several
woven motifs because of this. Related research uses an object detection approach
with the Faster R-CNN model. In this research, a model was built using a
similarity and object detection approach. The most suitable approach for this
research will be selected and the best model will be selected based on its
performance metrics. The similarity model built is Siamese network and the
object detection models built are Faster R-CNN and YOLO. The Siamese network
similarity model that was built gave good results for relatively small datasets, the
contrastive loss result was 0.003368. However, the drawback is that it cannot
handle the problem of more than one motif object in one woven fabric, the angle
of taking the image greatly influences the prediction results, and the output is still
the distance between the similarities between the two motifs. The object detection
model was built to overcome problems that cannot be overcome by the Siamese
network. The object detection models built include Faster R-CNN X101-FPN and
YOLOv8. Hyperparameter tuning is carried out to obtain the most optimal
parameter configuration. The performance metrics used are mAP50, precision,
recall, and inference time. The mAP50 performance metric is a popular metric to
express the accuracy of an object detection model. Precision measures the
accuracy of the model in classifying samples as positive class. Recall measures
the model's ability to detect Positive samples (model sensitivity). Inference time is
the time needed for the model to process data to make predictions. The YOLOv8
model was chosen because it produces the highest mAP50, precision and recall,
namely 96.1%, 91.397% and 92.026% respectively. The fastest YOLOv8
inference time is 2.8ms. |
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