Choice of Activation Function in Convolutional Neural Networks for Person Re-Identification in Video Surveillance Systems

Chen, H.; Ihnatsyeva, S. A.; Bohush, Rykhard; Ablameyko, Sergey

doi:10.1134/s0361768822050036

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…The restriction of data sets reduces the confidence of the three models. Combined with the conclusion of the article [31], it is found that the Charis activation function has stronger feedback on image features, which is convenient for mapping the image eigenvalue to the next layer. The simulation process of planar feature mapping under the influence of the activation function was shown in Figure 7.…”

Section: Data Description and Analysismentioning

confidence: 76%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry.

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Section: Data Description and Analysismentioning

confidence: 76%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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“…Для устранения дефицита данных иногда прибегают к объединению существующих наборов или расширению тренировочной выборки за счет имеющихся изображений путем применения к ним каких-либо преобразований [1,2]. Равномерность распределения классов возможно обес печить только в процессе формирования набора данных.…”

Section: Introductionunclassified

Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video

Ihnatsyeva

Bohush

2023

Doklady Belorusskogo gosudarstvennogo universiteta informatiki

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To improve the person re-identification system accuracy, an integrated approach is proposed in the formation of a training sample for convolutional neural networks, which involves the use of a new image dataset, an increase in the training examples number using existing datasets, and the use of a number of transformations to increase their diversity. The created dataset PolReID1077 contains images of people that were obtained in all seasons, which will improve the correct operation of re-identification systems when the seasons change. Another PolReID1077 advantage is the video data use obtained from external and internal surveillance in a large number of different filming locations. Therefore, the people images in the created set are characterized by the variability of the background, brightness and color characteristics. Joining the created dataset with the existing CUHK02, CUHK03, Market-1501, DukeMTMC-ReID and MSMT17 sets made it possible to obtain 109 772 images for training. An increase in the variety of generated examples is achieved by applying a cyclic shift to them, eliminating color and replacing a fragment with a reduced copy of another image. The research results on estimating the accuracy of re-identification for the ResNet-50 and DenseNet-121 convolutional neural networks during their training, using the proposed approach to form a training sample, are presented.

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An Intelligent-Safe Network Traffic Distribution System in Cluster Mesh-Formations of Thermal-Technology Complexes

Lazarev

Vereikina

Sinyavskiy

2023

Communications in Computer and Information Science

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Choice of Activation Function in Convolutional Neural Networks for Person Re-Identification in Video Surveillance Systems

Cited by 10 publications

References 6 publications

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video

An Intelligent-Safe Network Traffic Distribution System in Cluster Mesh-Formations of Thermal-Technology Complexes

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