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학술지 Deep Learning and Detection Technique with Least ImageCapturing for Multiple Pill Dispensing Inspection
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저자
권혁주, 김휘강, 정성운, 이성학
발행일
202210
출처
Journal of Sensors, v.2022, pp.1-20
ISSN
1687-725X
출판사
Hindawi Publishing
DOI
https://dx.doi.org/10.1155/2022/2339188
협약과제
22ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
초록
In this study, we propose a method to effectively increase the performance of small-object detection using limited training data. We aimed at detecting multiple objects in an image using training data in which each image contains only a single object. Medical pills of various shapes and colors were used as the learning and detection targets. We propose a labeling automation process to easily create label files for learning and a three-dimensional (3D) augmentation technique that applies stereo vision and 3D photo inpainting (3DPI) to avoid overfitting caused by limited data. We also apply confidence-based nonmaximum suppression and voting to improve detection performance. The proposed 3D augmentation, 2D rotation, nonmaximum suppression, and voting algorithms were applied in experiments conducted with 20 and 40 types of pills. The precision, recall, individual accuracy, and combination accuracy of the experiment with 20 types of pills were 0.998, 1.000, 0.998, and 0.991, respectively, and those for the experiment with 40 types of pills were 0.986, 0.999, 0.985, and 0.940, respectively.
KSP 제안 키워드
Limited data, Multiple objects, Object detection, Three dimensional(3D), Voting algorithms, automation process, deep learning(DL), detection performance, detection techniques, nonmaximum suppression, stereo vision
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