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Md Foysal Haque(포이살 하크),Hye-Youn Lim(임혜연),Dae-Seong Kang(강대성) 한국정보기술학회 2019 Proceedings of KIIT Conference Vol.2019 No.6
최근에 심층 컨볼루션 뉴럴 네트워크는 객체를 검출하고 분류하는데 있어서 상당한 성능을 지니고 있습니다. 레이어의 복잡성을 감소시키기 위해 향상된 네트워크 구조를 기반으로 한 연구가 많이 이루어지고 있습니다. 본 논문에서는 검출 정확도를 높이기 위해 향상된 변형 컨벌루션 뉴럴 네트워크를 제안합니다. 제안하는 방법은 공간적 특징 국소화를 사용하여 특징맵들을 분류하고 추출하기 위해 증가된 학습 모듈을 사용합니다. 여기서 네트워크는 객체 검출을 위해 최신 성능의 네트워크를 수행하는 향상된 일반화 방법으로 구성되어 있습니다. The recent years deep convolutional neural network achieved impressive performance to classify and detect objects. Lots of research contributing to improving network architecture to reduce the complexity of the layers. In this work, to improve the detection accuracy, we designed an improved deformable convolutional neural network. The deformable convolutional neural network uses augment learning module to extracts and classify the feature maps by spatial feature localization. The network consists of improved generalization method that carries the network towards the state-of-the-art performance for the object detection task.
Residual Dense Block 기반 변형 가능한 합성곱 정렬 신경망을 이용한 비디오 초해상화 방법
이동호,이유호,천세진,전동산 한국멀티미디어학회 2023 멀티미디어학회논문지 Vol.26 No.5
Although video super resolution(VSR) is one of the essential technologies in the area of video processing using deep neural network(DNN), it is difficult for the VSR network to utilize the temporal correlation between consecutive input video frames. Recently, convolutional neural networks(CNN) based VSR methods show a significant improvement to generate high resolution(HR) videos from low resolution(LR) videos. In this paper, we propose a VSR method using deformable convolution based alignment network with residual dense block. It enables to the proposed network to use the correlated information from the intermediate feature maps for the purpose of improving the quality of VSR. Compared to the previous methods, experimental results show that the proposed method achieves both PSNR and SSIM by as much as 0.23dB and 0.006 on average, respectively.
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.
Object Recognition from Spherical Camera Images Based on YOLOv3
Tomohiro Kai,Humin Lu,Tohru Kamiya 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
The aging of Japan is remarkable, and attention has been focused on the use and utilization of assistive devices. One of them is electric wheelchair, which enables physical disability people to easily operate it using a handle or a joystick. However, accidents are occurring frequently with increasing demand by using electric wheelchair. Therefore, developing an autonomous electric wheelchair is required to reduce accidents such as maneuvering mistakes, reduce the accident rate, improve convenience, and reduce the burden on caregivers. In this paper, we focus on the recognition of obstacles and use panoramic images obtained from a spherical camera that can easily handle information from all directions at low cost. A spherical camera is attached to an electric wheelchair, and images are cut out from the sequential images obtained by running. For image analysis, YOLOv3, which has been successful in the field of image recognition in recent years, is used. In the proposed method, considering the distortion of the image caused by using the spherical camera, the improvement of the model of YOLOv3 is examined, and the validity with the actual data is verified.