Automated vehicle counting using image processing and machine learning

Meany, Sean; Eskew, Edward; Martínez-Castro, Rosana Elena; Jang, Shinae

doi:10.1117/12.2261251

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Data processing using the nearest neighbours algorithm is done with Wolfram Mathematica 11.3 software using a computer with Intel Core i7-8750H (16 GB RAM) processor specifications. The use of Wolfram Mathematica software in this study is because the software can run a machine learning program and can be used to run a simple image processing algorithm [19] [20]. The nearest neighbours method is done using 27 training data.…”

Section: Methodsmentioning

confidence: 99%

Melastoma Malabathricum L. Extracts-Based Indicator for Monitoring Shrimp Freshness Integrated With Classification Technology Using Nearest Neighbours Algorithm

Noor

Safitri

et al. 2020

Sinergi

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As a maritime country, shrimp commodity production in Indonesia is very high and continues to increase. However, because shrimp is a perishable food, we need a detection device. This is because conventional methods that are widely used by the community in detecting freshness of shrimp are only based on the smell. Of course, this is a problem when shrimp are packed in closed containers. In this paper, a method for detecting shrimp is proposed using the Melastoma malabathricum L. - based label indicator. The high content of flavonoids in the extracts allows the changing the colour of the label from red to grey due to the interaction between the label with the OH- group that arises from the shrimp spoilage process. The colour that appears on the label indicator will correlate with the level of shrimp freshness. By increasing detection effectiveness, the classification is performed using the nearest-neighbours algorithm, which is equipped with an image processing mechanism in the form of colour quantization. There are four classifications used to express the quality of shrimp, namely "acceptable," "just acceptable," "unacceptable," and "more unacceptable." The accuracy of applying this method is 71.9%, with the majority of detection errors occurring in the "acceptable" class. Based on these results, it can be stated that the label indicators prepared in this study are very promising to be developed into intelligent packaging components.

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Section: Methodsmentioning

confidence: 99%