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9. Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

Author

Yaying Shi, Jiayi Li, Zeyun Yu, Yin Li, Yangpingqing Hu, and Lushen Wu

Subjects
barley seed detection, deep learning, object detection, Yolov5, Chemical technology, TP1-1185
Abstract

As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024 s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images.

Published
2022
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13. Enhancing DataRaceBench for Evaluating Data Race Detection Tools

Author

Yaying Shi, Yonghong Yan, Gaurav Verma, Barbara Chapman, and Chunhua Liao

Subjects
Java, Computer science, Fortran, business.industry, Suite, Dashboard (business), Benchmarking, Multithreading, Benchmark (computing), Code (cryptography), Software engineering, business, computer, computer.programming_language
Abstract

DataRaceBench is a dedicated benchmark suite to evaluate tools aimed to find data race bugs in OpenMP programs. Since its initial release in 2017, DataRaceBench has been widely used by tool developers to find the strengths and limitations of their tools. The results also provide an apple-to-apple comparison of the state-of-the-art of data race detection tools. In this paper, we discuss our latest efforts to enhance DataRaceBench. In particular, we have added support for Fortran language and some of the newest OpenMP 5.0 language features. We have also added new kernels representing new patterns from literature and other benchmarks (e.g., NAS Parallel Benchmark). To reduce duplicated code patterns in the benchmark suite, we have designed a distance-based code similarity analysis, combining both static and dynamic code features. Finally, we dockerize tools and streamline the entire benchmarking process to quickly generate a dashboard showing the state-of-the-art of data race detection of OpenMP programs. The enhanced DataRaceBench is released as v 1.3.0, with 222 newly added benchmarks. 56 of them are in C, and the remaining 166 are in Fortran, reproducing the C programs' nature. Our experiments show that this new version can spot more limitations of the current data race detection tools, with significantly reduced user efforts needed to run experiments.

Published
2020

14. Ompparser: A Standalone and Unified OpenMP Parser

Author

Chunhua Liao, Xinyao Yi, Bronis R. de Supinski, Anjia Wang, Yaying Shi, and Yonghong Yan

Subjects
Parsing, Syntax (programming languages), Fortran, Programming language, Computer science, Semantics (computer science), Software_PROGRAMMINGTECHNIQUES, computer.software_genre, Supercomputer, Formal grammar, Component (UML), Compiler, Software_PROGRAMMINGLANGUAGES, computer, computer.programming_language
Abstract

OpenMP has been quickly evolving to meet the insatiable demand for productive parallel programming on high performance computing systems. Creating a robust and optimizing OpenMP compiler has become increasingly challenging due to the expanding capabilities and complexity of OpenMP, especially for its latest 5.0 release. Although OpenMP’s syntax and semantics are very similar between C/C++ and Fortran, the corresponding compiler support, such as parsing and lowering are often separately implemented, which is a significant obstacle to support the fast changing OpenMP specification. In this paper, we present the design and implementation of a standalone and unified OpenMP parser, named ompparser, for both C/C++ and Fortran. ompparser is designed to be useful both as an independent tool and an integral component of an OpenMP compiler. It can be used for syntax and semantics checking of OpenMP constructs, validating and verifying the usage of existing constructs, and helping to prototype new constructs. The formal grammar included in ompparser also helps interpretation of the OpenMP standard. The ompparser implementation supports the latest OpenMP 5.0, including complex directives such as metadirective. It is released as open-source from https://github.com/passlab/ompparser with a BSD-license. We also demonstrate how it is integrated with the ROSE’s open-source OpenMP compiler.

Published
2019

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