Garbage localization based on weakly supervised learning in Deep Convolutional Neural Network

2018 ◽  
Author(s):  
Mohd Anjum ◽  
M. Sarosh Umar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Supervised Learning ◽  
Deep Convolutional Neural Network ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals Surface Defects Inspection of Cylindrical Metal Workpieces Based on Weakly Supervised Learning

2021 ◽  
Author(s):  
Mu Ye ◽  
Weiwei Zhang ◽  
Guohua Cui ◽  
Xiaolan Wang
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Metal Surface ◽  
Vision System ◽  
Global Maximum ◽  
Strong Light ◽  
Weakly Supervised Learning ◽  
Cylindrical Metal ◽  
Segmentation Accuracy ◽  
Weakly Supervised

Abstract In industrial vision system, metal surface is anisotropic under light in all directions and it is inevitable to cause local overexposure due to the natural reflection of active strong light, especially on the cylindrical metal surface. In this paper, injector valve is taken as the representative of cylindrical metal workpieces. Since the variety and complexity of cylindrical metal workpieces defects, and its contrast with the background of workpieces fluctuates, making samples annotating time-consuming and be of high cost. In order to solve the above challenges, this paper proposes an end-to-end weakly supervised learning framework to classify and segment defects. Firstly, a deep integrated residual attention convolutional neural network (IRA-CNN) is designed. IRA-CNN is composed of multiple IRA-Block. Two residual maps are included in IRA-Block to improve its bilateral nonlinearity and the robustness. IRA-block adds integrated attention module (IAM) which includes channel attention submodule and spatial attention submodule. The channel attention submodule adaptively extracts information from the global average pool layer and the global maximum pool layer to obtain the channel attention feature map. IAM can be well integrated into the IRA-CNN makes the neural network suppress the interference of useless background area and highlight the defect area. Finally, the weakly supervised segmentation method relies on Grad-CAM++ to generate saliency map to improve segmentation accuracy. The experimental results show that the accuracy of defect classification reaches 97.7\% and the segmentation accuracy is significantly improved compared with the benchmark method in the injector valve dataset which include 6747 images.

scholarly journals Deep Convolutional Neural Network with Structured Prediction for Weakly Supervised Audio Event Detection

2019 ◽  
Vol 9 (11) ◽  
pp. 2302 ◽  
Author(s):  
Inkyu Choi ◽  
Soo Hyun Bae ◽  
Nam Soo Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Event Detection ◽  
Large Scale ◽  
Prediction Method ◽  
Deep Convolutional Neural Network ◽  
Structured Prediction ◽  
Temporal Position ◽  
Audio Event ◽  
Weakly Supervised

Audio event detection (AED) is a task of recognizing the types of audio events in an audio stream and estimating their temporal positions. AED is typically based on fully supervised approaches, requiring strong labels including both the presence and temporal position of each audio event. However, fully supervised datasets are not easily available due to the heavy cost of human annotation. Recently, weakly supervised approaches for AED have been proposed, utilizing large scale datasets with weak labels including only the occurrence of events in recordings. In this work, we introduce a deep convolutional neural network (CNN) model called DSNet based on densely connected convolution networks (DenseNets) and squeeze-and-excitation networks (SENets) for weakly supervised training of AED. DSNet alleviates the vanishing-gradient problem and strengthens feature propagation and models interdependencies between channels. We also propose a structured prediction method for weakly supervised AED. We apply a recurrent neural network (RNN) based framework and a prediction smoothness cost function to consider long-term contextual information with reduced error propagation. In post-processing, conditional random fields (CRFs) are applied to take into account the dependency between segments and delineate the borders of audio events precisely. We evaluated our proposed models on the DCASE 2017 task 4 dataset and obtained state-of-the-art results on both audio tagging and event detection tasks.

scholarly journals Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images

2022 ◽  
Author(s):  
Xiaofeng Xie ◽  
Chi-Cheng Fu ◽  
Lei Lv ◽  
Qiuyi Ye ◽  
Yue Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Pleural Effusion ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Deep Convolutional Neural Network ◽  
Learning Method ◽  
Weakly Supervised

AbstractLung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019–9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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