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CARVING-DETC: A network scaling and NMS ensemble for Balinese carving motif detection method

Visual Informatics 2023年第3期7卷 1-10页

作者： wayan agus surya darma Nanik Suciati Daniel Siahaan Department of Informatics Faculty of Technology and InformaticsInstitut Bisnis dan Teknologi IndonesiaDenpasar80225Indonesia Department of Informatics Faculty of Intelligent Electrical and Informatics TechnologyInstitut Teknologi Sepuluh NopemberSurabaya60111Indonesia

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 ischallenging 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 onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese 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.

关键词： Balinese carving Object detection Network scaling Non-maximum suppression Ensemble model

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