Garbage classification system based on improved ShuffleNet v2

Chen, Zhichao; Yang, Jie; Chen, Lifang; Jiao, Haining

doi:10.1016/j.resconrec.2021.106090

Cited by 115 publications

(58 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e classification effects of a total of seven representative CNN standard models [15][16][17][18][19][20][21] (all with a single optimizer and a single loss function) containing AlexNet, GoogleNet, ResNet, VGG, EfficientNet, Mobile-Net, and ShuffleNet are collected during the determination of the numerical model for weld seam recognition, and the classification accuracy of each model is shown in Table 2, where ResNet is the model with the highest accuracy for weld seam recognition among all algorithms in the table . Based on the ResNet model and using the driven strategy in Figure 2, the classification accuracy obtained was higher (1.6% improved) compared to the plain ResNet model. 3 is the confusion matrix of the "ResNet + multistage training strategy model."…”

Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

In terms of the problems of five categories of nonweld seam stripes, including inclusion, oil-spot, silk-spot, and water-spot, which interfere with weld seam recognition during robotic welding, a convolutional neural network (CNN) algorithm, combined with a multistage training strategy, is used to construct a digital model for weld seam recognition, on the basis of which the classification accuracy is compared with the standard model of seven categories of representative CNN. The results show that the ResNet model with a multistage training strategy classifies weld seams with an accuracy of 83.8%, which is superior to other standard models. In this study, the physical scenario of weld seam recognition is migrated to a neural network digital model, fulfilling the intelligent recognition of weld seams in complex scenarios based on the CNN digital model.

show abstract

Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…Goo-gleNet-based operation for the group convolutions, ResNetbased function for the skip connection, and Xception-based operation for the depth-wise separable convolution. e group convolutions of point-wise convolution along with the structure of residual shortcut path is the ShuffleNet proposed as an approach [21].…”

Section: Shufflenet-v2-cnn Modelmentioning

confidence: 99%

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

Doss

Ramakrishnan

Kavitha

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

e silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. ere are some failure patterns that can be displayed by wafer maps. ese patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learningbased silicon wafer defect identi cation and classi cation model is proposed. e main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. is proposed model identi es and classi es the defects based on the wafer map images from the WM-811K dataset. e proposed model is composed of a pretrained deep transfer learning model called Shu eNet-v2 with convolutional neural network (CNN) architecture. is Shu eNet-v2-CNN performs the defects identi cation and classi cation process following the work ow of data preprocessing, data augmentation, feature extraction, and classi cation. For performance evaluation, the proposed Shu eNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. e proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images.

show abstract

“…Resent years have witnessed remarkable progresses in the technology of computer vision, with a variety of approaches propose in the literatures [9][10][11][12] and applied in the real applications relevant to garbage classification. The below researches mainly focus on one particular kind of garbage, using computer version to classify or clean specific garbage.…”

Section: Related Workmentioning

confidence: 99%

“…Zeng Wei et al [5] proposed a plastic bottle recognition and location algorithm based on computer vision, which has strong practicality for waste plastic target detection in complex environments. Zhichao Chen et al [14] proposed a Garbage image classification algorithm based on improved MobileNets v2, which embedded channel and space attention modules to achieve the goal of high detection speed and prediction accuracy. It could solve the problem of low speed and accuracy existing in the current garbage image classification model.…”

Section: Related Workmentioning

confidence: 99%

Waste Collection and Transportation Supervision Based on Improved YOLOv3 Model

Chen

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Supervising the process of garbage collection and transportation is a very crucial task with significance implication to the reusing and the recycle of the garbage. Previous works mainly focus on the detection of certain kind of garbage instead of overseeing the whole process. This paper proposed a supervision approach based on the improved YOLOv3 to decrease the incidence of unexpected mixing of the different kinds of garbage during the transportation and the collection which will cause the inferior performance of waste classification. Firstly, to reduce the parameters and arithmetic operations of YOLOv3 and improving the model's detection speed, we displaced the standard convolution in YOLOv3 by depthwise separable convolution. Secondly, to solve the problem that YOLOv3 has poor location accuracy and performs poorly in muti-targets, triplet attention is introduced into the backbone, which increases almost no parameters to automatically learn cross-dimensional interactions, enhance the effective feature channel weights, and strengthen the feature extraction capability. Finally, we built a dataset of waste classification supervision using the images provided by a city environmental protection bureau on which we did massive experiments. The experimental result shows that compared with other detection algorithms, the improved YOLO v3 algorithm has better performance. The mAP is 98.5%, which is 0.7% and 1.1% higher than the YOLOv5l and the EfficientDet-B0, respectively, and the average detection speed of the model is 14.6ms/it, which meets the requirements of regulatory real-time and environment complexity of the supervision system.

show abstract

Garbage classification system based on improved ShuffleNet v2

Cited by 115 publications

References 18 publications

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

Waste Collection and Transportation Supervision Based on Improved YOLOv3 Model

Contact Info

Product

Resources

About