scholarly journals Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qing An ◽  
Xijiang Chen ◽  
Xiaoyan Du ◽  
Jiewen Yang ◽  
Shusen Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Crack ◽  
Concrete Surface ◽  
High Accuracy ◽  
Detection Methods ◽  
Crack Identification ◽  
Recognition Method ◽  
Accurate Identification ◽  
Long Time ◽  
Crack Recognition

For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.

A vision-based active learning convolutional neural network model for concrete surface crack detection

2020 ◽  
Vol 23 (13) ◽  
pp. 2952-2964
Author(s):  
Zhen Wang ◽  
Guoshan Xu ◽  
Yong Ding ◽  
Bin Wu ◽  
Guoyu Lu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Learning Ability ◽  
Training Method ◽  
And Training ◽  
New Framework

Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

scholarly journals Traffic marking recognition based on generating antagonistic neural network

2019 ◽  
Vol 136 ◽  
pp. 04076 ◽  
Author(s):  
Shuwei Xu ◽  
Shan Zhang ◽  
Shuwei Xu
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Methods ◽  
Image Detection ◽  
Fast Recognition ◽  
Repeated Sampling ◽  
Backward Propagation ◽  
Traditional Image

This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.

scholarly journals Crack Identification on the Fresh Chilli (Capsicum) Fruit Destemmed System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quoc-Khanh Huynh ◽  
Chi-Ngon Nguyen ◽  
Hong-Phuc Vo-Nguyen ◽  
Phuong Lan Tran-Nguyen ◽  
Phan-Hung Le ◽  
...  
Keyword(s):  
Neural Network ◽  
Mekong Delta ◽  
Network Models ◽  
Application Programming Interface ◽  
Crack Identification ◽  
Neural Network Models ◽  
Accurate Identification ◽  
Identification Rate ◽  
Classification Image ◽  
Manual Sorting

Destemming fresh chilli fruit (Capsicum) in large productivity is necessary, especially in the Mekong Delta region. Several studies have been done to solve this problem with high applicability, but a certain percentage of the output consisted of cracked fruits, thus reducing the quality of the system. The manual sorting results in high costs and low quality, so it is necessary that automatic grading is performed after destemming. This research focused on developing a method to identify and classify cracked chilli fruits caused by the destemming process. The convolution neural network (CNN) model was built and trained to identify cracks; then, appropriate control signals were sent to the actuator for classification. Image processing operations are supported by the OpenCV library, while the TensorFlow data structure is used as a database and the Keras application programming interface supports the construction and training of neural network models. Experiments were carried out in both the static and working conditions, which, respectively, achieved an accurate identification rate of 97 and 95.3%. In addition, a success rate of 93% was found even when the chilli body is wrinkled due to drying after storage time at 120 hours. Practical results demonstrate that the reliability of the model was useful and acceptable.

Image Recognition and Counting for the Bacilli Cell Based on the Microscopic Image

2014 ◽  
Vol 651-653 ◽  
pp. 2318-2321
Author(s):  
Min Tan ◽  
Ji Kang Zhong ◽  
Guo Zhao Zhang ◽  
Zhi Xiang Hu
Keyword(s):  
Neural Network ◽  
Small Area ◽  
Object Segmentation ◽  
Single Layer ◽  
Difficult Problem ◽  
Microscopic Image ◽  
Neural Network Classifier ◽  
Recognition Method ◽  
Accurate Identification ◽  
Intelligent Recognition

In order to automatically detect bacilli in sputum image with microscopy, an intelligent recognition method based on machine vision is presented. Firstly, a novel background filter was designed based on the single layer perceptron to realize object segmentation from background. After eliminating the short twig and small area noise, the suspicious goals and the image noise are separated. In the feature extraction, besides the base features of single bacillus two important features are presented to solve the difficult problem of identification and counting for the overlapping and winding bacilli cells. Finally, an EBP neural network classifier is designed for the accurate identification and counting of the bacilli cells. Experimental results verified the effectiveness of the presented method.

scholarly journals Identifikasi Kemurnian Daging Berbasis Analisis Citra

2021 ◽  
Vol 8 (4) ◽  
pp. 643
Author(s):  
Nila Susila Yulianti ◽  
Kudang Boro Seminar ◽  
Joko Hermanianto ◽  
Sri Wahjuni
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wild Boar ◽  
Detection System ◽  
Detection Methods ◽  
Population Census ◽  
Muslim Population ◽  
Image Deformation ◽  
Long Time ◽  
Wild Boar Meat

<p class="Judul2">Daging sapi merupakan salah satu sumber protein hewani yang diperlukan oleh tubuh. Pada tahun 2015 dan 2016 konsumsi daging sapi per kapita sebesar 0,417 kg dan terjadi kenaikan pada tahun 2017 yaitu 12,50 % sebesar 0,469 kg. Sementara harga rata-rata daging sapi di tahun 2015 sebesar Rp 104 747 per kg dan mengalami kenaikan pada tahun 2016 yaitu 8,41 % sebesar Rp 113 555 per kg.  Di tahun 2017 kembali terjadi kenaikan yaitu 2,09 % sebesar 115 932 per kg. Berdasarkan sensus penduduk tahun 2010 mendata jumlah penduduk muslim sebesar 207176162 yaitu 87 % dari total penduduk di Indonesia. Kekhawatiran daging halal sangat penting di negara mayoritas muslim. Metode secara konvensional dengan uji laboratorium untuk mendeteksi daging celeng membutuhkan waktu yang relatif lama, tempat khusus, serta biaya yang relatif mahal. Sementara daging yang diwaspadai dicampur dengan daging babi hutan bisa terjadi di berbagai tempat seperti pasar, retailer serta  distributor yang sepatutnya bisa dideteksi seketika di tempat tersebut secara cepat. Oleh karena itu, diperlukan sistem yang mudah, cepat, dan mudah dibawa untuk mendeteksi daging sapi murni (tanpa campuran daging lainnya) dalam penelitian ini adalah daging celeng.</p><p class="Paragraf">Paper ini membahas metode deteksi daging campuran berbasis citra menggunakan <em>Convolutional Neural Network </em>(CNN) yang dapat dioperasikan di android. Keunggulan metode ini dapat melakukan proses pembelajaran secara mandiri yaitu ekstraksi citra dan klasifikasi, adapun kemampuan lain yang dimiliki yaitu dapat menangani deformasi gambar seperti translasi, rotasi dan skala. Akurasi yang didapatkan dari metode ini yaitu 94 % untuk mendeteksi daging sapi murni, daging celeng murni, dan daging campuran sapi dan celeng. Sementara presisi untuk celeng, campuran dan sapi yaitu 100 %, 90 % dan 95 %. Selain itu, <em>recall </em>untuk celeng, campuran dan sapi yaitu 85 %, 95 %, dan 97,5 %. Prototipe sistem deteksi yang dikembangkan telah diimplementasikan pada platform android dan diuji pada situasi pencahayaan yang masih terkondisikan. Upaya penyempurnaan ke depan adalah menambah fitur sistem pencahayaan  khusus/standar dengan kamera khusus yang memiliki cahaya tambahan yang mengatasi keragaman tingkat pencahayaan di tempat terbuka.</p><p class="Paragraf"> </p><p class="Paragraf"><em><br /></em></p><p class="Paragraf" align="center"><strong><em>Abstract</em></strong></p><p><em>Beef is one of animal protein source that important for human body. In 2015 and 2016 beef consumption per capita was 0.417 kg and it was increasing in 2017 by 12.50 % (i.e., 0.469 kg). While The average price of beef  at Rp 104 747 per kg in 2015 and went up  by 8,41 % at Rp 113 555 per kg in 2016. In 2017, there was an increase by 2,09 % at Rp 115 932 per kg. The increase of beef price average occurred in 2015 amounting to Rp 104 747 per kg and an increase in 2016 that was 8.41% amounting to Rp 113 555 per kg. Based on the population census in 2010 recorded a Muslim population of 207176162 which is 87% of the total population in Indonesia. The concern of halal (lawful) meat is very critical in the muslim majority country. The conventional method with laboratory testing to detect wild boar meat requires a relatively long time, a special place, and a relatively expensive cost. While meat that is mixed with wild boar can happen in various places such as markets, retailers and distributors which can be detected immediately in that place quickly.Therefore, a system that can be easily, quickly and portably used for detecting pure beef (without other mixed meat) in this study is wild boar.  </em></p><p><em>This paper discusses image-based mixed meat detection methods using the Convolutional Neural Network (CNN) that can be operated on android. so the proposed computationally method is Convolutional Neural Network (CNN). The advantages of this method can do the learning process independently, object extraction and classification, while the other capabilities that can handle image deformation such as translation, rotation, and scale. This method yields an overall accuracy of 94% for detecting pure beef, pure wild boar meat, and mixed beef and wild boar. The obtained precision values for wild boar, mixed meat and beef  are by 100 %, 90 % and 95 % respectively. Moreover, the values recall for wild boar, mixed meat and beef are by 85 %, 95 % and 97,5 % respectively. The prototype detection system developed has been implemented on the Android platform and tested in a lighting situation that is still conditioned. A  future effort to improve is providing   special / standard lighting with a special camera that has additional light that can overcome the diversity of levels of exposure in the open areas.</em></p><p> </p><p class="Paragraf"><em><br /></em></p>

scholarly journals Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Liming Li ◽  
Shubin Zheng ◽  
Chenxi Wang ◽  
Shuguang Zhao ◽  
Xiaodong Chai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Detection Method ◽  
Similarity Index ◽  
Area Under The Curve ◽  
Saliency Map ◽  
Crack Identification ◽  
Crack Recognition ◽  
Low Efficiency

This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). The improved YOLOv3 network is used to identify and locate cracks on sleepers and segment them after the sleeper on the ballast bed is extracted by using the gray projection method. The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. The accuracy of the crack identification model is improved by using a mixed loss function of binary cross-entropy (BCE), structural similarity index measure (SSIM), and intersection over union (IOU). Results show that this method can accurately detect the sleeper crack image. During object detection, the proposed method is compared with YOLOv3 in terms of directly locating sleeper cracks. It has an accuracy of 96.3%, a recall rate of 91.2%, a mean average precision (mAP) of 91.5%, and frames per second (FPS) of 76.6/s. In the crack extraction part, the F-weighted is 0.831, mean absolute error (MAE) is 0.0157, and area under the curve (AUC) is 0.9453. The proposed method has better recognition, higher efficiency, and robustness compared with the other network models.

Pengakuan Pendapatan dan Biaya dalam Kaitannya dengan Penentuan Laba Perusahaan Ekspedisi (Studi Kasus pada PT. EMKL Jelutung Subur)

2019 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Yuli Anwar
Keyword(s):  
Accounting Standards ◽  
Financial Accounting ◽  
Recognition Method ◽  
Important Thing ◽  
Storage Service ◽  
Long Time ◽  
Accrual Basis

Revenue and cost recognitions is the most important thing to be done by an entity,  time and the recognition method must be based on the rules from Financial Accounting Standards. Revenue and cost recognition which is done by PT. EMKL Jelutung Subur located on Pangkalpinang, Bangka Belitung province is done by using the accrual basis, and it can be seen with its influences to company profits every year.  This research is useful to get a data and information for preparing this thesis and improving my knowledge and also for comparing between theories accepted against facts applied in the field.  The result of this research shows that PT. EMKL Jelutung Subur has implemented one of the revenue and cost recognition method (accrual basis) continually, so that profit accuracy is accountable to be used for developing this kind of expedition business in order to become a better company. The accuracy is evaluated because all revenues received and cost spent  have clear evidence and found in the period of time.  The evaluation shows there is one thing that miss from revenue and cost recognition done by PT. EMKL Jelutung Subur, that is charge to the customers who use the storage service temporary, because some customers keep their goods for a long time in the warehouse, and it will increase the costs of loading, warehouse maintenance, damaged goods and decreasing a quantity of goods. If the storage service is charged to the customers, PT. EMKL Jelutung Subur will earn additional revenue to cover all the expenses above

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