TY - JOUR
T1 - CARVING-DETC
T2 - A network scaling and NMS ensemble for Balinese carving motif detection method
AU - Darma, I. Wayan Agus Surya
AU - Suciati, Nanik
AU - Siahaan, Daniel
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/9
Y1 - 2023/9
N2 - Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs, each representing the values adopted by the Balinese people. Detection of Balinese carving motifs is challenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance, and tiny-size carving motifs. This research aims to improve carving motif detection performance on challenging Balinese carving motifs detection task through a modification of YOLOv5 to support a digital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinese carving detection method consisting of three steps. First, the data generation step performs data augmentation and annotation on Balinese carving images. Second, we proposed a network scaling strategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the model ensemble to generate the most optimal predictions. The ensemble model utilizes NMS to produce higher performance by optimizing the detection results based on the highest confidence score and suppressing other overlap predictions with a lower confidence score. Third, performance evaluation on scaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conserving the cultural heritage and as a reference for other researchers. In addition, this study proposed a novel Balinese carving dataset through data collection, augmentation, and annotation. To our knowledge, it is the first Balinese carving dataset for the object detection task. Based on experimental results, CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.
AB - Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs, each representing the values adopted by the Balinese people. Detection of Balinese carving motifs is challenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance, and tiny-size carving motifs. This research aims to improve carving motif detection performance on challenging Balinese carving motifs detection task through a modification of YOLOv5 to support a digital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinese carving detection method consisting of three steps. First, the data generation step performs data augmentation and annotation on Balinese carving images. Second, we proposed a network scaling strategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the model ensemble to generate the most optimal predictions. The ensemble model utilizes NMS to produce higher performance by optimizing the detection results based on the highest confidence score and suppressing other overlap predictions with a lower confidence score. Third, performance evaluation on scaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conserving the cultural heritage and as a reference for other researchers. In addition, this study proposed a novel Balinese carving dataset through data collection, augmentation, and annotation. To our knowledge, it is the first Balinese carving dataset for the object detection task. Based on experimental results, CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.
KW - Balinese carving
KW - Ensemble model
KW - Network scaling
KW - Non-maximum suppression
KW - Object detection
UR - http://www.scopus.com/inward/record.url?scp=85171461701&partnerID=8YFLogxK
U2 - 10.1016/j.visinf.2023.05.004
DO - 10.1016/j.visinf.2023.05.004
M3 - Article
AN - SCOPUS:85171461701
SN - 2543-2656
VL - 7
SP - 1
EP - 10
JO - Visual Informatics
JF - Visual Informatics
IS - 3
ER -