High-Throughput Fixed-Point Object Detection on FPGAs

2014 ◽  
Author(s):  
Xiaoyin Ma ◽  
Walid Najjar ◽  
Amit Roy-Chowdhury
Keyword(s):  
Fixed Point ◽  
Object Detection ◽  
High Throughput ◽  
Point Object

scholarly journals Evaluation and Acceleration of High-Throughput Fixed-Point Object Detection on FPGAs

2015 ◽  
Vol 25 (6) ◽  
pp. 1051-1062 ◽  
Author(s):  
Xiaoyin Ma ◽  
Walid A. Najjar ◽  
Amit K. Roy-Chowdhury
Keyword(s):  
Fixed Point ◽  
Object Detection ◽  
High Throughput ◽  
Point Object

Design of High-Throughput Fixed-Point Complex Reciprocal/Square-Root Unit

2010 ◽  
Vol 57 (8) ◽  
pp. 627-631 ◽  
Author(s):  
Dong Wang ◽  
Miloš D Ercegovac ◽  
Nanning Zheng
Keyword(s):  
Fixed Point ◽  
High Throughput ◽  
Square Root

scholarly journals TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery

2020 ◽  
Vol 11 ◽  
Author(s):  
Hao Lu ◽  
Zhiguo Cao
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
High Throughput ◽  
Graphics Processing Units ◽  
State Of The Art ◽  
Image Resolution ◽  
Plant Phenotyping ◽  
Art Object ◽  
Bounding Boxes ◽  
Computational Bottleneck

Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

scholarly journals A high-throughput fixed-point complex divider for FPGAs

2013 ◽  
Vol 10 (4) ◽  
pp. 20120879-20120879 ◽  
Author(s):  
Dong Wang ◽  
Pengju Ren ◽  
Leibo Liu
Keyword(s):  
Fixed Point ◽  
High Throughput

Hybrid Fixed-Point/Binary Deep Neural Network Design Methodology for Low-Power Object Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 388-400
Author(s):  
Jiun-In Guo ◽  
Chia-Chi Tsai ◽  
Jian-Lin Zeng ◽  
Shao-Wei Peng ◽  
En-Chih Chang
Keyword(s):  
Neural Network ◽  
Fixed Point ◽  
Low Power ◽  
Object Detection ◽  
Network Design ◽  
Design Methodology ◽  
Deep Neural Network

scholarly journals Soft Anchor-Point Object Detection

2020 ◽  
pp. 91-107 ◽  
Author(s):  
Chenchen Zhu ◽  
Fangyi Chen ◽  
Zhiqiang Shen ◽  
Marios Savvides
Keyword(s):  
Object Detection ◽  
Anchor Point ◽  
Point Object

scholarly journals Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kazuma Sakoda ◽  
Tomoya Watanabe ◽  
Shun Sukemura ◽  
Shunzo Kobayashi ◽  
Yuichi Nagasaki ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Object Detection ◽  
High Throughput ◽  
Stomatal Density

Application of conventional UAV-based high-throughput object detection to the early diagnosis of pine wilt disease by deep learning

2021 ◽  
Vol 486 ◽  
pp. 118986
Author(s):  
Bizhi Wu ◽  
Anjie Liang ◽  
Huafeng Zhang ◽  
Tengfei Zhu ◽  
Zhiying Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Object Detection ◽  
High Throughput ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt

QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

2021 ◽  
Author(s):  
Muhammad Gohar Javed ◽  
Minahil Raza ◽  
Muhammad Mohsin Ghaffar ◽  
Christian Weis ◽  
Norbert Wehn ◽  
...  
Keyword(s):  
Object Detection ◽  
High Throughput ◽  
Power Efficient
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