Detection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture

Arshaghi, Ali; Ashourin, Mohsen; Ghabeli, Leila

doi:10.18280/ts.380622

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…The sub-images (4,5,8) show the license plate recognition under different lighting scenes, and the experimental results show that the license plate recognition is accurate with recognition precision of 97.2%, 96.2% and 97.9%, respectively. Sub-images (1,3,6,7) show the license plate recognition under complex character conditions, which include consecutive identical characters, numbers and letters with similar shapes and Chinese abbreviations of different provinces in the license plate, and the experimental results show that the model has a good recognition effect. As shown above, the model proposed in this paper can accurately recognize license plate images in various scenes with strong stability and robustness.…”

Section: ) License Plate Recognition Modelmentioning

confidence: 99%

“…scenarios. In recent years, with the rapid development of computer hardware, neural network models based on deep learning have become the best tools to solve complex computer vision problems [1], [2], [3]. Convolutional Neural Network(CNN) is one of the best deep learning techniques for target detection and recognition tasks, and the most popular algorithm in CNN-based target detection is YOLO, proposed by Redmon in 2015 [4].…”

Section: Introductionmentioning

confidence: 99%

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License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi

Zhao

2023

IEEE Access

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Aiming at the problem that the traditional license plate recognition method lacking of accuracy and speed, an end-to-end deep learning model for license plate location and recognition in natural scenarios was proposed. First, we added an improved channel attention mechanism to the down-sampling process of the You only look once(YOLOv5). Additionally, a location information is added in the ones to minimize the information loss from sampling, which can improve the feature extraction ability of the model. Then we reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Finally, Gated recurrent units(GRU) + Connectionist temporal classification(CTC) was used to build the recognition network to complete the character segmentation-free recognition task of the license plate, significantly shortened the training time and improved the convergence speed and recognition accuracy of the network. The experimental results show that the average recognition precision of the license plate recognition model proposed in this paper reaches 98.98%, which is significantly better than the traditional recognition algorithm, and the recognition effect is good in complex environment with good stability and robustness.

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Section: ) License Plate Recognition Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi

Zhao

2023

IEEE Access

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“…It consists of a feature pyramid network (FPN), path aggregation network (PANet), and CSP2 networks [30]. The FPN transfers the semantic information from top to bottom layers whereas PANet propagates the localization details from bottom to top layers [35]. The PANet and FPN structures enable multiscale detection of the target regions by combining strong semantic and localization features from top-down and bottom-up layers [30].…”

Section: Yolov5 Detection Principles and Improvement A Yolov5mentioning

confidence: 99%

YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images

Vidhya,

Raghavendra,

Gudigar

et al. 2023

IEEE Access

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Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques for intracranial hematoma detection in non-contrast computed tomography images cannot effectively detect multiple lesions of irregular sizes and shapes. Therefore, a computer-aided system using different window settings, YOLOv5s, cascaded attention module, and spatial pyramid pooling-fast is proposed to detect hematoma types, namely acute intraparenchymal, intraventricular, subdural, epidural, subarachnoid, and chronic subdural. Firstly, the computed tomography images are pre-processed using a window-based stacking approach wherein a three-channel image is generated by stacking grayscale images obtained with the aid of multiple window settings, i.e, brain, bone, and subdural. Secondly, a cascaded attention module is constructed in the neck of the YOLOv5s model to improve its detection performance by placing the convolution block attention module in serial with the efficient channel attention module. The cascaded attention module enriches the feature representation of various hematoma types in complex backgrounds especially when they are small and inconspicuous. The spatial pyramid pooling is replaced by a spatial pyramid pooling-fast to reduce the computational parameters and accelerate the feature fusion ability. The proposed deep learning model is trained, validated, and tested with 15,921 images from the brain haemorrhage extended dataset and it achieved overall precision, recall, F1score, and mean average precision at 0.5 and 0.5:0.95 of 0.935, 0.908, 0.921, 0.943 and 0.65 respectively. The experimental results show that in comparison to the original YOLOv5s model and state-of-the-art methods, the model was able to localize and classify the acute or chronic instances of five hematoma subtypes in an individual image with improved precision and recall values. Hence the proposed system can be used in hospitals for the early and accurate detection of hematoma.INDEX TERMS Cascaded attention module, medical image processing, intracranial hematoma, TBI, YOLO.

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“…Compared to other studies, this research employs a GLCMbased image processing approach that has been popular and widely used in various domains, such as Recognition and Classification of Apple Leaf Diseases [22] , Plant Disease Classificationc [23], Leather Defect Detection and Classification [24], Apple Sorting [25], Potato Agricultural Product Defects [26], Tomato Leaf Diseases [27], enhancing chestnut quality [28], mango leaf variety classification [29], and Leaf Disease Detection [30].…”

Section: Introductionmentioning

confidence: 99%

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

Setiaji,

Adi,

Surarso

2024

ISI

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Eggs are widely consumed due to their high content of vitamin B12, choline and iron. External factors can be detected by observing the eggshell or taken into consideration. The nutrition in eggs is influenced by egg quality, which can be directly observed from the shell. If the shell is cracked, it can be inferred that the egg has poor quality because Salmonella bacteria are dangerous pathogens that can enter the egg. The current issue lies in the complexity and inefficiency of individually classifying eggs by workers, as it is a complicated, time-consuming, frustrating, and inefficient task. Therefore, it is important to separate them automatically. The selection of cracked and intact eggs in this research is an innovative approach to classification using a highly accurate machine learning method. The application of the GLCM-CNN method is an innovative strategy employed for selecting and classifying cracked eggs, as outlined in this research. VGG 19, one of the computational methods, is utilized as a comparative method alongside RESNET 50 and VGG 16. The GLCM-CNN algorithm in this research employed 1,000 images for each class, with a validation set of 20% for each class, resulting in an accuracy of 98%. The inefficient classification process and complexity of automated egg quality classification can be significantly addressed through the findings presented in this research.

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Detection and Classification of Potato Diseases Potato Using a New Convolution Neural Network Architecture

Cited by 12 publications

References 31 publications

License Plate Recognition System Based on Improved YOLOv5 and GRU

License Plate Recognition System Based on Improved YOLOv5 and GRU

YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images

Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method

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