[논문]MobileNetV2 기반의 개선된 Lightweight 모델을 이용한 열화도로 영상에서의 블랙 아이스 인식

이옥걸; 강선경

doi:10.6109/jkiice.2021.25.12.1835

MobileNetV2 기반의 개선된 Lightweight 모델을 이용한 열화도로 영상에서의 블랙 아이스 인식
A Black Ice Recognition in Infrared Road Images Using Improved Lightweight Model Based on MobileNetV2 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.25 no.12, 2021년, pp.1835 - 1845

이옥걸 (School of Intelligent Manufacturing, Weifang University of Science and Technology) , 강선경 (Department of Computer Software Engineering, Wonkwang University)

초록
AI-Helper

본 논문에서는 블랙 아이스를 정확하게 인식하고 도로 노면 정보를 운전자에게 미리 알려줘서 속도를 제어하고 예방 조치를 취할 수 있도록 하기 위해 열화 도로 영상을 기반으로 블랙 아이스 검출하기 위해 lightweight 네트워크를 제안한다. 전이학습을 이용하여 블랙 아이스 인식 실험을 하였고, 블랙 아이스 인식의 정확도 향상을 위해 MobileNetV2 기반의 개선된 lightweight 네트워크를 개발하였다. 계산량을 줄이기 위해 Linear Bottleneck 및 Inverted Residuals를 활용하여 4개의 Bottleneck 그룹을 사용하고 모델의 인식률 향상을 위해 각 Bottleneck 그룹에 3×3 컨볼루션 레이어를 연결하여 지역적 특징 추출을 강화하고 특징 맵의 수를 늘렸다. 마지막으로 구축된 블랙 아이스 데이터 세트 대상으로 블랙 아이스 인식 실험을 진행하였으며, 제안된 모델은 블랙 아이스에 대해 99.07%의 정확한 인식률을 나타내었다.

Abstract ▼ AI-Helper

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

주제어

표/그림 (12)

그림 Fig. 1 The Environment of Taking Images of Infrared Road Black Ice Experiment
그림 Fig. 2 The Samples of the Infrared Road Black Ice Dataset.
표 Table. 1 The Number of Infrared Black Ice Road Image Dataset
그림 Fig. 3 The Trends of Train Accuracy on Infrared Road Ice Dataset Using Transfer Learning
그림 Fig. 4 Inverted Residual Linear Block
표 Table. 2 The Comparison of Different Transfer Learning Network Performance
그림 Fig. 5 Improved Network Model Architecture
표 Table. 3 Improved Network Structure Parameters
표 Table. 4 The Parameters and Computation of the proposed Network
그림 Fig. 6 MobileNetV2 Training Accuracy
그림 Fig. 7 Proposed Model Training Accuracy
표 Table. 5 The Comparison of MobileNetV2 and Proposed Model Network Performance

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