[논문]가상 환경에서의 딥러닝 기반 폐색영역 검출을 위한 데이터베이스 구축

김경수; 이재인; 곽석우; 강원율; 신대영; 황성호

doi:10.7839/ksfc.2022.19.3.009

가상 환경에서의 딥러닝 기반 폐색영역 검출을 위한 데이터베이스 구축
Construction of Database for Deep Learning-based Occlusion Area Detection in the Virtual Environment 원문보기

드라이브ㆍ컨트롤 = Journal of drive and control, v.19 no.3, 2022년, pp.9 - 15

김경수 (Department of Mechanical Engineering, Sungkyunkwan University) , 이재인 (Department of Mechanical Engineering, Sungkyunkwan University) , 곽석우 (Department of Mechanical Engineering, Sungkyunkwan University) , 강원율 (Institute of Vehicle Engineering) , 신대영 (Korea Institute of Industrial Technology) , 황성호 (Department of Mechanical Engineering, Sungkyunkwan University)

Abstract ▼ AI-Helper

This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

주제어

표/그림 (15)

그림 Fig. 1 Road and graphic for Cheongna city
그림 Fig. 2 Image dataset from VTD simulator
그림 Fig. 3 Data acquisition for construction site
그림 Fig. 4 Process of training dataset from VTD
표 Table 1 Driving scenario
그림 Fig. 5 Image transformation with top view
그림 Fig. 6 Preprocessing of occlusion area
그림 Fig. 7 (a) Original image, (b) Result by open datasets, (c) Result by VTD datasets
그림 Fig. 8 (a) Front image, (b) Reference image, (c) Detected image
그림 Fig. 9 (a) Bus stop image, (b) Result by open datasets, (c) Result by VTD datasets
그림 Fig. 10 (a) Original image, (b) Result by open datasets, (c) Result by VTD datasets
그림 Fig. 11 (a) Top view reference image, (b) GT image with occlusion, (c) Detected image
그림 Fig. 12 Result of dealing occlusion
그림 Fig. 13 Grid map with detection of occlusion
그림 Fig. 14 Structure of DBMS

참고문헌 (10)

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