First Step Towards Creating a Software Package for Detecting the Dangerous States During Driver Eye Monitoring

Andriyanov, Nikita

doi:10.1007/978-3-030-68821-9_29

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…First phase: identifying individual tubers in the image. Because the system must process 4-5 images with a resolution of 3840 × 2160 pixels per second, the Viola-Jones method [58][59][60][61][62][63] is the most suitable method for detecting tubers. But even taking into account the high performance of this algorithm, the processing of such an image format will take an unacceptably long time.…”

Section: Resultsmentioning

confidence: 99%

“…We proposed using the Viola-Jones algorithm at the first stage of processing the image from a video camera, which, unlike convolutional neural networks, works in a real-time mode [58][59][60][61][62][63]. This method was created for recognizing human faces and did not give good results when used to detect potato tubers; however, by selecting preprocessing filters, we achieved a probability of 97%, which corresponds to the results of a convolutional neural network (from 91 to 95% in works on convolutional networks for the last three years) [25][26][27][28][29][30][31].…”

Section: Discussionmentioning

confidence: 99%

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Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

et al. 2021

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The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems.

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