A Convolutional Neural Network for Leaves Recognition Using Data Augmentation

Zhang, Chaoyun; Zhou, Pan; Li, Chenghua; Liu, Lijun

doi:10.1109/cit/iucc/dasc/picom.2015.318

Cited by 82 publications

(33 citation statements)

References 14 publications

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“…Comparing the classification results on the Flavia dataset with the work performed by other authors, it can be seen that Satti et al [48] presented an accuracy of 85.9% and 93.3% for KNN and ANN classifiers, respectively, while Zhang et al [49] presented a table comparing the accuracy values with 13 other author’s schemes (on the same Flavia dataset). The accuracy average of the works presented in that table was 87.37%.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Pereira

Morais

Reis

2019

Sensors

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Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Pereira

Morais

Reis

2019

Sensors

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“…Each subset was an independent combination of 400 patients for training and 100 patients for test images, to prevent the mixing of patient images between the training and testing images within the subsets. To effectively learn for the training dataset, we performed data augmentation ( 19 , 20 ) using image rotation from −15° to 15° in 3° steps and a zoom rate from 0.9 to 1.1 in 0.1 steps.…”

Section: Methodsmentioning

confidence: 99%

Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

et al. 2020

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Deep learning technology is now used for medical imaging. YOLOv2 is an object detection model using deep learning. Here, we applied YOLOv2 to FDG-PET images to detect the physiological uptake on the images. We also investigated the detection precision of abnormal uptake by a combined technique with YOLOv2. Using 3,500 maximum intensity projection (MIP) images of 500 cases of whole-body FDG-PET examinations, we manually drew rectangular regions of interest with the size of each physiological uptake to create a dataset. Using YOLOv2, we performed image training as transfer learning by initial weight. We evaluated YOLOv2's physiological uptake detection by determining the intersection over union (IoU), average precision (AP), mean average precision (mAP), and frames per second (FPS). We also developed a combination method for detecting abnormal uptake by subtracting the YOLOv2-detected physiological uptake. We calculated the coverage rate, false-positive rate, and false-negative rate by comparing the combination method-generated color map with the abnormal findings identified by experienced radiologists. The APs for physiological uptakes were: brain, 0.993; liver, 0.913; and bladder, 0.879. The mAP was 0.831 for all classes with the IoU threshold value 0.5. Each subset's average FPS was 31.60 ± 4.66. The combination method's coverage rate, false-positive rate, and false-negative rate for detecting abnormal uptake were 0.9205 ± 0.0312, 0.3704 ± 0.0213, and 0.1000 ± 0.0774, respectively. The physiological uptake of FDG-PET on MIP images was quickly and precisely detected using YOLOv2. The combination method, which can be utilized the characteristics of the detector by YOLOv2, detected the radiologist-identified abnormalities with a high coverage rate. The detectability and fast response would thus be useful as a diagnostic tool.

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“…Although less popular, there are other excellent deep learning libraries, such as CNTK, 9 Deeplearning4j, 10 Blocks, 11 Gluon, 12 and Lasagne, 13 which can also be employed in mobile systems. Selecting among these varies according to specific applications.…”

Section: Dedicated Deep Learning Librariesmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

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1,377

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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A Convolutional Neural Network for Leaves Recognition Using Data Augmentation

Cited by 82 publications

References 14 publications

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

Deep Learning in Mobile and Wireless Networking: A Survey

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