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Conference Paper: Vision Guided Crop Detection in Field Robots using FPGA-Based Reconfigurable Computers
Title | Vision Guided Crop Detection in Field Robots using FPGA-Based Reconfigurable Computers |
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Authors | |
Keywords | Agriculture Field programmable gate arrays Graphics processing units Robots Neural networks |
Issue Date | 2020 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089 |
Citation | Proceedings of 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, Spain, 10-21 October 2020, p. 1-5 How to Cite? |
Abstract | A case study in applying modern FPGAs as a platform to accelerate intelligent vision-guided crop detection in agricultural field robots is presented. A state-of-the-art YOLOv3 object detection neural network was adapted to detect broccoli and cauliflower in image dataset obtained from autonomous agricultural robots. A baseline floating point implementation achieved 96% mAP, and an efficient, quantized implementation suitable for FPGA implementation 92% mAP. The proposed FPGA solution has 136.86 ms inference latency while consuming 12.43W in a low latency configuration, and 28.48 frames per second while consuming 17.78W in a high throughput one. Compared to an embedded GPU implementation of the same task, the FPGA solution was 4.12 times more power-efficient and offers 6.85 times higher throughput, translating to faster and longer operation of a battery-powered field robot. |
Persistent Identifier | http://hdl.handle.net/10722/289416 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Chan, CWH | - |
dc.contributor.author | Leong, PHW | - |
dc.contributor.author | So, HKH | - |
dc.date.accessioned | 2020-10-22T08:12:21Z | - |
dc.date.available | 2020-10-22T08:12:21Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Sevilla, Spain, 10-21 October 2020, p. 1-5 | - |
dc.identifier.issn | 2158-1525 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289416 | - |
dc.description.abstract | A case study in applying modern FPGAs as a platform to accelerate intelligent vision-guided crop detection in agricultural field robots is presented. A state-of-the-art YOLOv3 object detection neural network was adapted to detect broccoli and cauliflower in image dataset obtained from autonomous agricultural robots. A baseline floating point implementation achieved 96% mAP, and an efficient, quantized implementation suitable for FPGA implementation 92% mAP. The proposed FPGA solution has 136.86 ms inference latency while consuming 12.43W in a low latency configuration, and 28.48 frames per second while consuming 17.78W in a high throughput one. Compared to an embedded GPU implementation of the same task, the FPGA solution was 4.12 times more power-efficient and offers 6.85 times higher throughput, translating to faster and longer operation of a battery-powered field robot. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089 | - |
dc.relation.ispartof | IEEE International Symposium on Circuits and Systems (ISCAS) | - |
dc.rights | IEEE International Symposium on Circuits and Systems (ISCAS). Copyright © IEEE. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Agriculture | - |
dc.subject | Field programmable gate arrays | - |
dc.subject | Graphics processing units | - |
dc.subject | Robots | - |
dc.subject | Neural networks | - |
dc.title | Vision Guided Crop Detection in Field Robots using FPGA-Based Reconfigurable Computers | - |
dc.type | Conference_Paper | - |
dc.identifier.email | So, HKH: hso@eee.hku.hk | - |
dc.identifier.authority | So, HKH=rp00169 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1109/ISCAS45731.2020.9181302 | - |
dc.identifier.hkuros | 316793 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 5 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0271-4302 | - |