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학술대회 Pig Datasets of Livestock for Deep Learning to Detect Posture using Surveillance Camera
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김유진, 박대헌, 박현, 김세한
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1196-1198
20HU1100, 축산질병 예방 및 통제 관리를 위한 ICT 기반의 지능형 스마트 안전 축사 기술 개발, 김세한
This paper proposes the pig Datasets for deep learning to detect a posture using a surveillance camera. The proposal aims to develop the best quality Datasets that can be input to the object detection pipeline for building a deep learning model. The proposed Datasets have two types with 7 and 9 categories(each called Class 7 and Class 9), and each evaluated. In Class 9, one of the categories labels to the ground- truth bounding box as a fake data assuming that the hidden head of a pig. The hidden head of pig shows to decrease performance. When training YOLOv2 and SSD against proposed Datasets, the performance an average precision (AP) of each training result evaluates the trained model with three types of optimization algorithms: Adam, SGDM, and RMSProp. The detection accuracy for proposed Datasets Class 7 reaches 97%. YOLOv2 using the proposed Datasets of Class 7 shows seven times better performance than SSD.
KSP 제안 키워드
Average Precision, Bounding Box, Detection accuracy, Learning model, Object detection, Optimization algorithm, deep learning(DL), ground truth, surveillance camera