A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

Khrissi, Lahbib; Akkad, Nabil El; Satori, Hassan; Satori, Khalid

doi:10.47839/ijc.21.2.2584

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…It's crucial to remember that the original centroids chosen can have an impact on how well the clustering outcome turns out. It is usual practice to run K -means numerous times with different initializations and choose the optimal outcome according to a criterion like minimizing the total within-cluster variance 30 . This is because random initialization can occasionally result in inadequate solutions.…”

Section: K-means Algorithmmentioning

confidence: 99%

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Moussaoui,

Akkad,

Benslimane

et al. 2024

Sci Rep

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Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

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Section: K-means Algorithmmentioning

confidence: 99%

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Moussaoui,

Akkad,

Benslimane

et al. 2024

Sci Rep

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“…Step 3: Define every foreground object by using some of the morphological techniques such as, erosion, closing, dilation and opening [33,34,35].…”

Section: Marker Watershed Algorithmmentioning

confidence: 99%

A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods

Moussaoui

Akkad

Benslimane

2023

Stat., optim. inf. comput.

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Presently image segmentation remains the most crucial stage in the image processing system. The main idea ofimage segmentation is to partition or divide a random image into several partitions depending on the problem to solve. In this paper, we will be presenting a new method of skin cancer detection based on Otsu’s thresholding algorithm and markercontrolled watershed method. This hybridization process is first of all started by segmenting the input image using fuzzy c-means algorithm which is a clustering method that gives the possibility to a pixel to belong to one or more clusters. After that, we will apply multi-Otsu which is a thresholding algorithm that separates the pixels of an image into a variety of classes depending on the intensity of the gray levels. The next step of this proposed method is the marker-controlled watershedalgorithm that divides the image into homogenous areas or regions by using edge-detection concepts including mathematical morphology. The proposed technique was applied and experienced using several images of different types of skin cancer that were collected and gathered from the web and also from the Kaggle dataset. To assess the worth of the achieved results, we used several evaluation metrics like dice coefficient, sensitivity, specificity as well as Jaccard similarity that all have shown good and satisfactory results.

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“…The second type is called variable thresholding, where the value T changes over an image, which means we will have more than one T value. The third type is Local or regional thresholding [42], which is when the T value at any point (x, y) in the image relies on the properties of a neighborhood of (x, y). the last type is dynamic or adaptive thresholding, here the variable T depends on the spatial coordinates (x, y).…”

Section: Fig 7 E-elan Architecturementioning

confidence: 99%

Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

Moussaoui

Akkad

Benslimane

2023

Preprint

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License plate detection has always been challenging, especially with the increasing number of smart radars on roads and highways. By 2021, over 1 billion smart traffic radars have been provided worldwide for the gendarmerie as well as for road control officers from the National Security. This paper introduces a novel Arabic and Latin license plate detection and recognition method. In the first stage of the proposed method and after gathering images to build a new dataset, we used YOLOv7 to detect and localize the license plate in the image. The dataset has been labeled manually before feeding it to the detection system. Second, we applied some of the machine learning algorithms to enhance the detected license plate. For this, we employed thresholding as well as the kernel algorithms in order to remove the additional vertical lines in the plate. Afterward, we used the Arabic OCR as well as Easy OCR techniques to recognize the Arabic and the Latin letters on the license plate. The proposed detection algorithm achieved a precision of 97% and a recall of 98%, consequently, we achieved an F1 score of 98% in license plate detection. For image segmentation, while using Arabic OCR and Easy OCR to segment and extract characters from the detected license plate, we achieved an accuracy of 99 %. The proposed method gave satisfactory results for both detection and segmentation.

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A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

Cited by 12 publications

References 35 publications

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

A Hybrid Skin Lesions Segmentation Approach Based on Image Processing Methods

Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods

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