Comprehensive Pineapple Segmentation Techniques with Intelligent Convolutional Neural Network

<span>This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method.</span>

Download Full-text

A High Accuracy Multiple-Command Speech Recognition ASIC Based on Configurable One-Dimension Convolutional Neural Network

2021 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas51556.2021.9401401 ◽

2021 ◽

Author(s):

Lindong Wu ◽

Zongwei Wang ◽

Ming Zhao ◽

Wei Hu ◽

Yimao Cai ◽

...

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Convolutional Neural Network ◽

High Accuracy ◽

One Dimension

Download Full-text

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽

10.3390/s21010262 ◽

2021 ◽

Vol 21 (1) ◽

pp. 262

Author(s):

Chih-Yung Huang ◽

Zaky Dzulfikri

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Template Matching ◽

Health Condition ◽

Vibration Measurement ◽

One Dimensional ◽

Stamping Process ◽

Metalworking Industry ◽

Increasing Demand ◽

Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

Download Full-text

Binary Convolutional Neural Network with High Accuracy and Compression Rate

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence ◽

10.1145/3377713.3377721 ◽

2019 ◽

Author(s):

Songwei Liu ◽

Hongwei Zhu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Accuracy ◽

Compression Rate

Download Full-text

Study on Rapid Archival Technology of Bullets Based on Graph Convolutional Neural Network

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2022.66.4.040401 ◽

2021 ◽

Author(s):

Shi-bo Pan ◽

Di-lin Pan ◽

Nan Pan ◽

Xiao Ye ◽

Miaohan Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Dynamic Time Warping ◽

Large Scale ◽

Line Graph ◽

High Accuracy ◽

Time Warping ◽

Large Numbers ◽

Dynamic Time

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

Download Full-text

A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST

International Journal of Reconfigurable and Embedded Systems (IJRES) ◽

10.11591/ijres.v10.i1.pp1-10 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

Kenta Shirane ◽

Takahiro Yamamoto ◽

Hiroyuki Tomiyama

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Design Methodology ◽

Critical Path ◽

High Accuracy ◽

Path Delay ◽

Trade Off ◽

Critical Path Delay

In this paper, we present a case study on approximate multipliers for MNIST Convolutional Neural Network (CNN). We apply approximate multipliers with different bit-width to the convolution layer in MNIST CNN, evaluate the accuracy of MNIST classification, and analyze the trade-off between approximate multiplier’s area, critical path delay and the accuracy. Based on the results of the evaluation and analysis, we propose a design methodology for approximate multipliers. The approximate multipliers consist of some partial products, which are carefully selected according to the CNN input. With this methodology, we further reduce the area and the delay of the multipliers with keeping high accuracy of the MNIST classification.

Download Full-text

Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽

10.3390/inventions6040070 ◽

2021 ◽

Vol 6 (4) ◽

pp. 70

Author(s):

Elena Solovyeva ◽

Ali Abdullah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Networks ◽

Low Cost ◽

Computational Cost ◽

High Accuracy ◽

Activation Functions ◽

Fully Connected ◽

Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

Download Full-text

A Single Target Grasp Detection Network Based on Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/5512728 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Longzhi Zhang ◽

Dongmei Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

High Accuracy ◽

Experimental Results ◽

Detection Accuracy ◽

Single Object ◽

Single Target ◽

High Detection ◽

Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

Download Full-text

APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR DETECTION OF MELANOMA USING SKIN LESION IMAGE ON MOBILE DEVICE

Ukrainian Journal of Information Technology ◽

10.23939/ujit2021.03.008 ◽

2021 ◽

Vol 3 (1) ◽

pp. 8-14

Author(s):

D. V. Fedasyuk ◽

◽

T. V. Demianets ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Mobile Device ◽

Mobile Application ◽

High Accuracy ◽

File Size ◽

Melanoma Detection ◽

The Neural Network ◽

Classification Time

A melanoma is the deadliest skin cancer, so early diagnosis can provide a positive prognosis for treatment. Modern methods for early detecting melanoma on the image of the tumor are considered, and their advantages and disadvantages are analyzed. The article demonstrates a prototype of a mobile application for the detection of melanoma on the image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.

Download Full-text

Emotion Recognition in Horses with Convolutional Neural Networks

Future Internet ◽

10.3390/fi13100250 ◽

2021 ◽

Vol 13 (10) ◽

pp. 250

Author(s):

Luis A. Corujo ◽

Emily Kieson ◽

Timo Schloesser ◽

Peter A. Gloor

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Intelligent Systems ◽

High Accuracy ◽

Eye Position ◽

Proof Of Concept ◽

Concept System ◽

Validation Set ◽

Fast Region ◽

Head Neck

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. In total, 400 images of horses were collected and labeled to train both the detector and the model while 40 were used to test the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through the head, neck, ear, muzzle, and eye position. The system showed an accuracy of 80% on the validation set and 65% on the test set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

Download Full-text