화재의 환경적 물리적, 화학적, 구조적 배경에 관한 과학적 감식과 조사체계의 구체화 및 인적, 물적 손실의
예방과 안전화의 학문과 기술발전을 도모하고, 산,학,연 정의 상호교류를 통한 화재조사 및 감식의 전문화와
함께 소방관련 정책방향 발전에 공헌하며, 소방 분야 종사자들 간의 정보교환의 장과 사기양양 및 친목도모를
그 목적으로 한다.
한국화재감식학회 학회지 (2020)
확장된 CNN기반 감시 시스템의 초기 화재 및 연기 감지 모델
The technologies underlying fire and smoke detection systems play crucial roles in ensuring that these systems deliver optimal performance in modern. In fact, fire can cause significant damage to lives and properties. In majority of cities, camera-monitoring systems have been already installed, to take advantage of availability of these kinds of systems encourage us to develop a cost-effective vision detection methods. However, this is a complex vision task by the reason of perspective deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a method-based on deep learning that uses a convolutional neural network, which employs dilated convolutions. We evaluated our method, by training and testing it on our custom-built dataset. Consisting of a collection of fire and smoke images that we collected and labeled manually. The performances of methods proposed in previous studies were compared with those of well-known state-of-the-art architectures; our experimental results indicate that the classification performance and complexity of our method was superior to those of previous methods. In addition, our method is designed to be well generalized for unseen data, that it offers effective generalization and also reduces the number of false alarms.