Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Zhu, Yi-hua; Duan, Jiameng; Li, Yunhong; Wu, Tong

doi:10.1177/00405175211046060

Cited by 10 publications

(2 citation statements)

References 13 publications

(43 reference statements)

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“…Finally, the improved random forest algorithm was used to classify images. The research outcomes demonstrated that the IR model made the classification accuracy of cashmere and wool higher, about 90% 5 . Zhu et al designed an IR method for cashmere and wool fibers based on an improved Xception network.…”

Section: Related Workmentioning

confidence: 96%

The application of improved densenet algorithm in accurate image recognition

Hou,

Wu,

Cai

et al. 2024

Sci Rep

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Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.

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Section: Related Workmentioning

confidence: 96%

The application of improved densenet algorithm in accurate image recognition

Hou,

Wu,

Cai

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…Its complex depth structure is the reason for the slowdown of YOLOv3 training and detection 28 . Five down-sampling times are carried out in the network, and the features are output in the last three layers, and three scales of YOLO layers are generated after processing in the pyramid feature space 29 , 30 . Darknet-53 dominates the feature extraction work, and the YOLO layer is responsible for the interaction between the different feature layers.…”

Section: Improved Yolo Lightweight Model Design For Intelligent Stati...mentioning

confidence: 99%

Hybrid YOLOv3 and ReID intelligent identification statistical model for people flow in public places

Zheng

2024

Sci Rep

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The statistical model for automatic flow recognition is significant for public place management. However, the current model suffers from insufficient statistical accuracy and low lightweight. Therefore, in this study, the structure of the lightweight object detection model "You Only Live Once v3" is optimized, and the "Deep Simple Online Real-Time Tracking" algorithm with the "Person Re-Identification" module is designed, so as to construct a statistical model for people flow recognition. The results showed that the median PersonAP of the designed model was 94.2%, the total detection time was 216 ms, the Rank-1 and Rank-10 were 87.2% and 98.6%, respectively, and the maximum occupied memory of the whole test set was 2.57 MB, which was better than all comparison models. The results indicate that the intelligent identification statistical model for public crowd flow obtained through this design and training has higher statistical accuracy, less computational resource consumption, and faster computing speed. This has certain application space in the management and guidance of crowd flow in public places.

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Voltage‐based fault arc detection based on PCA‐RF

Wu,

Wang,

Peng

et al. 2024

Circuit Theory & Apps

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SummaryThe arc fault characteristics of certain loads lack significance, making it difficult to efficiently detect the line current characteristics. This research presents a novel approach for detecting arc faults using a combination of principalc analysis (PCA) and Random Forest (RF) based on voltage measurements. The time‐domain eigenvalues of the load terminal voltages of single and mixed loads are initially extracted during both arc fault and normal operation. Principal component analysis is then conducted on a subset of these eigenvalues. The skewness and magnitude features of the resulting principal components and load terminal voltages are utilized as inputs for the Random Forest algorithm. After training the model, classification results are obtained. Ultimately, it is contrasted with techniques such as rime optimization algorithm‐multilayer perceptron (RIME‐MLP), convolutional neural network‐gated recurrent unit‐SE attention (CNN‐GRU‐SE), and Kepler optimization algorithm‐support vector machine (KOA‐SVM). The results demonstrated that the approach exhibits superior accuracy and a reduced false alarm rate.

show abstract

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Cited by 10 publications

References 13 publications

The application of improved densenet algorithm in accurate image recognition

The application of improved densenet algorithm in accurate image recognition

Hybrid YOLOv3 and ReID intelligent identification statistical model for people flow in public places

Voltage‐based fault arc detection based on PCA‐RF

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