Quality Control of Cigarettes Packaging using Convolutional Neural Network

Nazar, Akif; Nurwiyadi, M N P; Syai’in, Mat; Khumaidi, Agus; Adhitya, Ryan Yudha; Rinanto, Noorman; Hasin, Muhammad Khoirul; Widodo, Hanan

doi:10.1088/1757-899x/462/1/012002

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Como se puede observar para esta época las marcas con mayor relevancia en el mercado eran Marlboro, Mustang, Piel Roja, Boston, Belmont y Kool. Situación que en el presente ha cambiado, debido a que los dueños de la empresa British American Tobacco (BAT), que era la encargada de producir los sellos Mustang y Belmont, decidieron cambiarlos por los sellos Rothmans azul y gris respectivamente 57 . Esto afecto sus ventas en un principio y permitió posicionar en la competencia a los cigarrillos de la marca Lucky strike como unos de los más comunes.…”

Section: Identificación De Caracteristicas Para Definir Colillas Objetivounclassified

Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Nguyen

Lin

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

206

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Section: Identificación De Caracteristicas Para Definir Colillas Objetivounclassified

Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Nguyen

Lin

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

206

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“…The basic ability that must be possessed by the goalkeeper robot is detecting the ball in the field. In this paper, camera sensor is used to detect the object and personal computer to process digital images captured by camera (Khumaidi, et al, 2017) (Nazar, et al, 2009). The ball detection process begins with the conversion of RGB to HSV color space (Marzuqi, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

Setiawan

Khumaidi

Priyonggo

et al. 2019

ATCSJ

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In this research Trigonometry Technique was implemented to predict the ball movement direction for Wheeled Soccer Robot Goalkeeper. The performance of goalkeeper robot in Wheeled Soccer Robot Contest is very important. The crucial problem with goalkeeper robot is the delay in ball detection by the camera because the results of the camera images captured are always slower than the pictures that have been captured. This causes the robot's response to blocking the opponent's kick ball being late. Trigonometry Technique is one technique that can be used to predict the direction of the ball movement based on trigonometry mathematical formulas. The input data used is the location of the last ball position (x–last ball and y-last ball) and the location of the current ball position (x-current ball and y-current ball). The outputs are the prediction of the next ball location (x-predict ball and y-predict ball) and the prediction of ball movement direction prediction. The results are the goalkeeper's robot successfully predicts the opponent's kick direction with 90% accuracy and can predict the location of the next ball very well. By implementing this method, it is expected to optimize the performance of the goalkeeper robot in saving the goal.

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Object recognition on patrol ship using image processing and convolutional neural network (CNN)

Julianto¹,

Khumaidi²,

Priyonggo³

et al. 2020

J. Phys.: Conf. Ser.

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Autonomous patrol vessels are state border patrol vessels equipped with cameras and image processing capabilities to detect objects around them. This prototype of ship can recognize a detected object; it used an image classification method called Convolutional Neural Network (CNN). So, it will minimize the occurrence of accidents on patrol boats. Input image in the form of RGB will experience feature extraction using a convolution layer. In the classification layer there is an artificial neural network with backpropagation to classify objects against a predetermined dataset. The detection value of the obtained vessel is operated by a predetermined actuator. In the final classification results the object recognition in the form of ships have a quite high accuracy. The average accuracy value is 96.59 percent with a sufficient light condition and RGB image input is taken in real-time.

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Quality Control of Cigarettes Packaging using Convolutional Neural Network

Cited by 4 publications

References 14 publications

Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

Object recognition on patrol ship using image processing and convolutional neural network (CNN)

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