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(사)한국터널지하공간학회

ISSN : 2233-8292

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Vol.21 No.3

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Effect on self-enhancement of deep-learning inference by repeated training of false detection cases in tunnel accident image detection

(사)한국터널지하공간학회 / (사)한국터널지하공간학회, (P)2233-8292; (E)2287-4747
2019, v.21 no.3, pp.419-432
https://doi.org/10.9711/KTAJ.2019.21.3.419


& (2019). Effect on self-enhancement of deep-learning inference by repeated training of false detection cases in tunnel accident image detection. (사)한국터널지하공간학회, 21(3), 419-432, https://doi.org/10.9711/KTAJ.2019.21.3.419

Abstract

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for ‘True Positive’ data but not to reduce occurrence of ‘False Positive’ data. In this paper, the occurrence of unpredictable ‘False Positive’ appears by trained modes with labeling data and ‘True Positive’ data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of ‘False Positive’ to ‘fire’ or ‘person’ objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the ‘False Positive’ data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the ‘False Positive’ data shows that the number of ‘False Positive’ for the persons were more reduced in case of training model including many ‘False Positive’ data. By training of the ‘False Positive’ data, the capability of field application of the deep learning model was improved automatically.

keywords
오탐지 데이터, 레이블링 데이터, 딥러닝 기반 터널 CCTV 영상유고 시스템, 오탐지 데이터 포함 딥러닝 모델 학습, False Positive data, Labeling data, Deep learning-based CCTV incident detection system, Deep learning model training including False Positive data

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