Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify a...
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Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has attracted great attention because it can effectively classify and detect the image. A multi-class object detection system from a video or image is quite challenging because of the errors obtained by the location classification process. Our proposed system generalized a hybrid convolutional neural network (H-CNN) model is used to realize the user object from an image. The proposed work integrates pre-processing, object localization, feature extraction and classification. First, the input image is pre-processed with Gaussian filtering to remove noise and improve the image quality. After completing the pre-processing procedure, it is subjected to object localization. Here the object in the image is localized using Grid Guided Localization (GGL). In the feature extraction phase, the model would be pre-trained with AlexNet. Here the AlexNet are generalized as fully connected (FC) layers. Finally, the Softmax layer in the AlexNet architecture is replaced by SvR (Support vector Regression), which acts as a classifier for identifying the object class. The classification loss is minimized using the Improved Grey Wolf (IGW) optimization algorithm. Thus, the H-CNN model can quickly classify and label the objects from images. It also offers improved classification performance in managing effective training time. The proposed work will be implemented in PYTHON. Therefore, the model would be built using various datasets such as MIT-67, PASCAL vOC2010, MS (Microsoft)-COCO, and MSRC to effectively train and classify the object. The proposed H-CNN achieved improved results with MIT-67 (96.02%), PASCAL vOC2010 (95.04%), MSRC (97.37%), and MS COCO (94.53%). The results obtained by H-CNN proved that the excluded result of Mean Average Precision (mAP), Precision, Accuracy, Recall values and F1-Score achieved better results than with re
The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems...
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ISBN:
(纸本)9798350372977;9798350372984
The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.
Diabetic retinopathy (DR), a severe complication arising from diabetes, make a significant threat to vision due to the deterioration of retinal blood vessels. This research work proposes a comprehensive methodology fo...
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ISBN:
(纸本)9798350373301;9798350373295
Diabetic retinopathy (DR), a severe complication arising from diabetes, make a significant threat to vision due to the deterioration of retinal blood vessels. This research work proposes a comprehensive methodology for the automated detection, grading, and segmentation of DR, leveraging advanced imageprocessing, deep learning techniques and machine learning. The study utilizes the Indian Diabetic Retinopathy image dataset (IDRID), comprising 81 fundus images and labels, to rigorously evaluates the proposed methodology. Key steps include detailed image preprocessing, vGG16-based feature extraction, Random Forest classifier-based grading, and innovative segmentation techniques for lesion localization. The evaluation demonstrates exceptional performance, with both vGG16 and ResNet50 architectures achieving over 99% accuracy. The process of semantic segmentation enhances interpretability, supporting clinical decision-making in retinopathy diagnosis. While the results are promising, future validation on diverse datasets and careful consideration of ethical implications are essential for responsible deployment in clinical settings. The proposed methodology signifies a significant step toward precise diagnostics and improved patient outcomes in diabetic retinopathy and holds potential for broader applications in retinal disease diagnosis.
The verification of IP core with imageprocessing algorithm is important for SoC and FPGA application in the field of machinevision. This paper proposes a verification framework with general purpose, real-time perfor...
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In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources,...
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In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local im...
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In recent years there has been an increased interest towards edge computing, i.e., computing performed on distributed devices as opposed to centralized high-power hubs. Examples of edge computing would be the local imageprocessing performed on Unmanned Autonomous vehicles (UAv's) or the specialized machinevision systems on drones. These edge computing applications require schemes that are efficient with power and memory and typically must operate real-time. Many state-of-the-art imageprocessing solutions that employ advanced optimization and deep neural networks (NNs) achieve impressive benchmark results, but are computationally demanding and thus on many occasions, impractical. The additional requirement for a range of applications is noise robustness or the ability to work in (extreme) low-light conditions; reasonable quality image or accurate object classification may be critical when there is low light flux or when the environment is over-saturated with other signals. Here, we approach edge computing with a combination of optical preprocessing and shallow NN and we show that this hybrid approach greatly reduces the computational requirements. For low-SNR imaging, we develop a technique that reconstructs objects and scenes from their Fourier-plane images. The optical preprocessing is performed via encoded diffraction with optical vortex singularities. The optical vortex encoder achieves differentiation of the already-compressed Fourier-plane patterns and enables facile inverse inference of the original object scene. We demonstrate that our method is robust to noise. And for a simple NN architecture (one or two layers), leads to generalization, i.e., reconstruction of objects from classes that are greatly different from the ones the NN was trained on. Our research identifies strong potential for swift hybrid imaging systems with edge computing applications and highlights the valuable function of the vortex encoder for spectral differentiation.
The fashion industry’s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanc...
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This research work suggests developing a diagnostic tool by using the techniques of machine learning and computer vision for the identification of plant diseases based on leaf images. It incorporates various features ...
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Convolution is a fundamental operation in imageprocessing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundar...
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The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic&#...
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