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RISS 인기검색어
- 검색키워드
- 저자 : Yuta Nishida (검색결과 2 건)
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Environment Recognition from Spherical Camera Images Based on Multi-Attention DeepLab
Yuta Nishida,Li Guangxu,Huimin Lu,Tohru Kamiya 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Electric wheelchair is an easy-to-operate means of transportation that does not require physical strength. With the number of electric wheelchair users increasing in recent years, the increase in traffic accidents becomes a problem. Therefore, by developing an autonomous electric wheelchair, it is expected that the risk of accidents will be reduced and the convenience of the electric wheelchair will be improved. Environment recognition is indispensable for the development of autonomous electric wheelchairs. We propose a semantic segmentation method for recognizing 16 objects in traffic environment. This paper examines the improvement of problems such as the high price of autonomous electric wheelchairs due to the increase in the number of sensors used, which has been a concern in related research. Therefore, we use panoramic images acquired by a spherical camera as input data, and extern the Multi-Attention Deep Lab algorithms fitting for the recognition of distorted images. A new CNN model is constructed sing Deep Lab v3+, scSE Block, Pairwise Self-Attention, and Joint Pyramid Up-sampling. We conducted a recognition experiment using images taken on campus and verified its effectiveness. (Comparing to DeepLab v3+, IoU and Dice showed a 3.5% and 3.6% improvement in accuracy, respectively.)
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Environment Recognition from A Spherical Camera Image Based on DeepLab v3+
Yuta Nishida,Yujie Li,Tohru Kamiya 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The number of users of electric wheelchairs has been increasing in recent years because it is easy to operate the electric wheelchair and do not require physical strength. However, the traffic accidents are also increasing because of the large number of wheelchairs. The development of autonomous electric wheelchairs is expected to reduce the risk of accidents and improve the convenience of electric wheelchairs. Environmental recognition is essential for the development of autonomous electric wheelchairs. In this paper, we propose a method for recognizing roads, sidewalks, buildings, electric wheelchair drivers, poles, electric wheelchairs, vegetation, curbs, sky, pedestrians, lanes, cars, steps, and bicycles. For recognizing those objects, we use a panoramic image acquired from a spherical camera. As the machine techniques, we use DeepLab v3+, a semantic segmentation algorithm based on Convolutional Neural Network (CNN). In the proposed method, a new CNN model is constructed by adding deformable convolution, SE-block, and MobileNet v2 to DeepLab v3+ into the original DeepLab v3+. In the experiment, IoU 38.8% and Dice of 46.7% were obtained.
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