Convolutional neural network applied for object recognition in a warehouse of an electric company

Piratelo, Paulo Henrique Martinez; Azeredo, Rodrigo Negri de; Yamao, Eduardo Massashi; Maidl, Gabriel; Jesus, Laercio Pereira de; Neto, Renato de Arruda Penteado; Coelho, Leandro dos Santos; Leandro, Gideon Villar

doi:10.1109/induscon51756.2021.9529716

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In deep learning algorithms, a CNN is a kind of network architecture that is used specifically for tasks such as image recognition [ 30 ]. The CNN is extensively used in disease diagnosis [ 31 ], speech recognition [ 32 ], object classification [ 33 ], and fault detection [ 34 ]. A CNN needs to simplify the images without removing important details in order to provide reliable results [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

View full text Add to dashboard Cite

Due to the tremendous expectations placed on batteries to produce a reliable and secure product, fault detection has become a critical part of the manufacturing process. Manually, it takes much labor and effort to test each battery individually for manufacturing faults including burning, welding that is too high, missing welds, shifting, welding holes, and so forth. Additionally, manual battery fault detection takes too much time and is extremely expensive. We solved this issue by using image processing and machine learning techniques to automatically detect faults in the battery manufacturing process. Our approach will reduce the need for human intervention, save time, and be easy to implement. A CMOS camera was used to collect a large number of images belonging to eight common battery manufacturing faults. The welding area of the batteries’ positive and negative terminals was captured from different distances, between 40 and 50 cm. Before deploying the learning models, first, we used the CNN for feature extraction from the image data. To over-sample the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE) since the dataset was highly imbalanced, resulting in over-fitting of the learning model. Several machine learning and deep learning models were deployed on the CNN-extracted features and over-sampled data. Random forest achieved a significant 84% accuracy with our proposed approach. Additionally, we applied K-fold cross-validation with the proposed approach to validate the significance of the approach, and the logistic regression achieved an 81.897% mean accuracy score and a +/− 0.0255 standard deviation.

show abstract

Section: Methodsmentioning

confidence: 99%

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Din

Zhang

Yang

2023

Sensors

View full text Add to dashboard Cite

show abstract

Convolutional neural networks for identification of moving combustion chambers entering a brazing process

Pereira¹,

Breda²,

Pinho³

et al. 2023

Procedia Computer Science

View full text Add to dashboard Cite

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

View full text Add to dashboard Cite

Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

show abstract

Convolutional neural network applied for object recognition in a warehouse of an electric company

Cited by 3 publications

References 17 publications

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

Convolutional neural networks for identification of moving combustion chambers entering a brazing process

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Contact Info

Product

Resources

About