A study on development of a hybrid aerial terrestrial robot system for avoiding ground obstacles by flight

Premachandra, Chinthaka; Otsuka, Masahiro; Gohara, Ryo; Ninomiya, T.; Kato, K.

doi:10.1109/jas.2018.7511258

Cited by 37 publications

(22 citation statements)

References 35 publications

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“…We can also try to implement this architecture in another field or train using different datasets to find out its capability as well as implementing it in mobile application. Furthermore, some robotic applications have been conducted using light-weight hardware [37][38][39][40][41][42]. In the future, we plan to apply our proposal in such kind of object detection applications.…”

Section: Discussionmentioning

confidence: 99%

Small and Slim Deep Convolutional Neural Network for Mobile Device

2020

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Recent development of deep convolutional neural networks (DCNN) devoted in creating a slim model for devices with lower specification such as embedded, mobile hardware, or microcomputer. Slim model can be achieved by minimizing computational complexity which theoretically will make processing time faster. Therefore, our focus is to build an architecture with minimum floating-point operation per second (FLOPs). In this work, we propose a small and slim architecture which later will be compared to state-of-the-art models. This architecture will be implemented into two models which are CustomNet and CustomNet2. Each of these models implements 3 convolutional blocks which reduce the computational complexity while maintains its accuracy and able to compete with state-of-the-art DCNN models. These models will be trained using ImageNet, CIFAR 10, CIFAR 100 and other datasets. The result will be compared based on accuracy, complexity, size, processing time, and trainable parameter. From the result, we found that one of our models which is CustomNet2, is better than MobileNet, MobileNet-v2, DenseNet, NASNetMobile in accuracy, trainable parameter, and complexity. For future implementation, this architecture can be adapted using region based DCNN for multiple object detection.

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Section: Discussionmentioning

confidence: 99%

Small and Slim Deep Convolutional Neural Network for Mobile Device

2020

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“…We have been developing different type of UAV systems for making applications in indoor mapping [20], and autonomous flight [21][22][23][24][25][26][27][28]. Fig.…”

Section: System Overwiewmentioning

confidence: 99%

Audio-Processing-Based Human Detection at Disaster Sites With Unmanned Aerial Vehicle

2020

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This paper describes a human search system that uses an unmanned aerial vehicle (UAV). The use of robots to search for people is expected to become an auxiliary tool for saving lives during a disaster. In particular, because UAVs can collect information from the air, there has been much research into human search using UAVs equipped with cameras. However, the disadvantage of cameras is that they struggle to detect people who are hidden in shadows. To solve this problem, we mounted an array microphone on a UAV and to detect the human voice as a means of finding people that cameras cannot. Also a search method is proposed that combines voice and camera human detection to compensate for their respective shortcomings. The rate and accuracy of human detection by the proposed method are assessed experimentally.

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“…Frame and background subtraction can be used to detect moving objects in images [21][22][23][24][25][26][27][28][29][30][31][32]. In frame subtraction, moving objects are detected by calculating the image subtraction between two or a few consecutive frames [21,23,28]. Moving objects must have some minimal speed for detection by frame subtraction.…”

Section: A Moving Object Candidate Extraction By Gaussian Mixture Momentioning

confidence: 99%

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

2020

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Many complicated road intersections are seen while driving. In some, blind spots make it difficult for drivers or automated vehicles to discern moving objects coming from certain directions, possibly confusing drivers or autonomous vehicles wishing to cross or to turn at the intersection. To address this problem, we investigate detection and tracking of all moving objects at an intersection using a single 360degree-view camera (3DVC). Through experiments, we develop methods allowing a 3DVC to capture the entirety of a four-way intersection when installed at one corner. This paper also presents image processing algorithms for detecting and tracking moving objects at intersections by processing images from the installed 3DVC. Experiments under varied conditions demonstrate that the proposed detection algorithm has a very high detection rate. We also confirm the tracking ability for moving objects detected using the proposed algorithm. INDEX TERMS 360-degree camera (Omnidirectional camera), driver assistance, autonomous driving, moving object detection and tracking, image conversion.

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A study on development of a hybrid aerial terrestrial robot system for avoiding ground obstacles by flight

Cited by 37 publications

References 35 publications

Small and Slim Deep Convolutional Neural Network for Mobile Device

Small and Slim Deep Convolutional Neural Network for Mobile Device

Audio-Processing-Based Human Detection at Disaster Sites With Unmanned Aerial Vehicle

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

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