Deteksi Mata Mengantuk Pada Pengemudi Mobil Menggunakan Metode Viola Jones

Imanuddin, Imanuddin; Maulana, Rahmat; Munawir, Munawir

doi:10.31328/jointecs.v4i2.1005

Cited by 5 publications

(4 citation statements)

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“…There is preprocessing detection where the image will experience an integral image. In moving video, the system will process the figure in a box consisting of several pixels which undergo calculation and addition of pixels continuously, therefore it needs an integral image to produce a fast process for correcting features in the image [8]. The feature value in Figure 7 is said to be eligible if the threshold value is below the ABFE square feature value, the system will make a decision whether the feature fulfill the requirements.…”

Section: Image Processing Process Haar Casecade Classifier Face Detec...mentioning

confidence: 99%

Detection System of Drowsiness for Car Driver Using Image Processing Completed with Multi Level Safety

Waty

Kholis

Baskoro

et al. 2021

Indones. J. Electr. Electron. Eng.

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The rate of road traffic accidents in the 38th and 39th week of 2020 has increased. Many factors influence the occurrence of accidents, one of the reasons is that the driver is sleepy. The parameters used to determine the condition of drowsiness are identifying the condition of the eyelids. With this identification, an image processing system can be utilized by applying the Haar Casecade Classifier and Circular Hough Transform methods with Find Countur using HSV parameters. The detection uses a camera with the driver's eye input, if the image detected is a drowsy eye, the system will wake up the driver by turning on a sound alarm, besides this system is equipped with an SMS gateway feature that will be sent to the driver's relatives, that the driver is sleepy. Message notification sent contains the condition and location of the rider. Based on system-wide testing, this system has a success rate of 100% against HSV1, 96% against HSV2 and HSV3, 89% against HSV4, and 88% against HSV5 on eye detection results and response time. The resulting error is because the lighting at a certain distance is not match with HSV set, the system experiences a freeze due to the buzzer sound delay that is too long and the performance of the PC is also less than optimal.

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Section: Image Processing Process Haar Casecade Classifier Face Detec...mentioning

confidence: 99%

Detection System of Drowsiness for Car Driver Using Image Processing Completed with Multi Level Safety

Waty

Kholis

Baskoro

et al. 2021

Indones. J. Electr. Electron. Eng.

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“…Menurut data statistik tanaman buah dan sayuran tahun 2017 yang dipublikasikan oleh BPS, jumlah total produksi apel pada tahun 2017 sebesar 319004 ton yang merupakan salah satu produksi tanaman buah terbesar di Indonesia [1]. Terdapat banyak penyakit yang dapat menyerang tanaman apel, oleh karena itu pendeteksian dini serta pengidentifikasian [2] penyakit tanaman mejadi faktor utama untuk mencegah dan mengurangi penyebaran penyakit tanaman apel. Dari permasalahan yang ada, penelitian ini bertujuan untuk mendeteksi penyakit apel dengan menggunakan citra daun apel yang pada prosesnya data training dilatih dengan melakukan 3 kali epoch yaitu 50, 75 dan 100 untuk menghasilkan tingkat akurasi yang maksimal.…”

Section: Pendahuluanunclassified

Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

Wicaksono¹,

Andryana²,

Benrahman³

2020

JOINTECS

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According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images.

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“…The initial indication of feeling tired. [3] is the inability to open the eyes [3] [4]. Frequently closing your eyes makes it impossible to carry out their tasks effectively.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the EAR Method in Detecting Drowsiness in Vehicle Drivers

Suryowinoto,

Rohman,

Pambudi

2024

jeeeu

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Based on data from Polda Metro Jaya Traffic Sector, the number of traffic accidents throughout 2020 was 7,565. With a factor of 1,018 drowsy drivers or 13% of the total incidents. A solution is needed for that. The aim of this research is to calculate the average value of the duration of detection time and the number of flashes per minute, by knowing when the driver is drowsy or not drowsy. The method used is Eye Aspect Ratio (EAR), where the driver will be detected whether he is sleepy or not, by analyzing the parameters of the number of blinks per minute and the duration of the blinks. If the eyes are open (EAR value) less than 0.45 and more than or equal to 3 seconds in one blink, it is categorized as sleepy. Tests were carried out on lighting, namely: morning, afternoon, evening and night light. Test results in daylight and evening light conditions with a light intensity value of 78 lux mean the duration of drowsiness detection is 3.26 seconds and the difference in time duration with the reference theory is 0.26 seconds. Meanwhile, for night light with a light intensity value of 15 lux, the average duration of drowsiness detection is 4.04 seconds and the difference in time duration with the reference theory is 1.04 seconds. Meanwhile, the number of blinks per minute when you are sleepy is 5-8 blinks/minute and when you are not sleepy it is 13-17 blinks/minute, for morning, afternoon, evening and night light conditions. It can be concluded that overall, this system can work well for day and evening and night light conditions.

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Deteksi Mata Mengantuk Pada Pengemudi Mobil Menggunakan Metode Viola Jones

Cited by 5 publications

References 0 publications

Detection System of Drowsiness for Car Driver Using Image Processing Completed with Multi Level Safety

Detection System of Drowsiness for Car Driver Using Image Processing Completed with Multi Level Safety

Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

Implementation of the EAR Method in Detecting Drowsiness in Vehicle Drivers

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