Jong Hyeok Mun, Do Hyung Kim, Jong Sun Choi, Jae Young Choi

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 4, pp. 133-142, Apr. 2021
https://doi.org/10.3745/KTSDE.2021.10.4.133,   PDF Download:
Keywords: Trusted Deep Learning, Model Reference, Deep Learning Description Language, Traffic Situation Analysis Model
Abstract

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

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