Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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Real-time 2D–3D door detection and state classification on a low-power device

SN Applied Sciences ◽

10.1007/s42452-021-04588-3 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

João Gaspar Ramôa ◽

Vasco Lopes ◽

Luís A. Alexandre ◽

S. Mogo

Keyword(s):

Low Power ◽

Real Time ◽

Object Classification ◽

Semantic Segmentation ◽

Detection Algorithm ◽

Power Device ◽

Indoor Environments ◽

State Classification ◽

Segmentation Algorithms ◽

Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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Real-time, High-resolution Depth Upsampling on Embedded Accelerators

ACM Transactions on Embedded Computing Systems ◽

10.1145/3436878 ◽

2021 ◽

Vol 20 (3) ◽

pp. 1-22

Author(s):

David Langerman ◽

Alan George

Keyword(s):

High Resolution ◽

Low Power ◽

Real Time ◽

Mixed Reality ◽

Graphics Processing Unit ◽

Processing Unit ◽

Reconfigurable Logic ◽

Depth Sensors ◽

Time Requirements ◽

Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

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