Auto-Detection Method Using Convolution Neural Network for Bottom-Simulating Reflectors

Xu, Haowei; Xing, Junhui; Yang, Boxue; Liu, Chuang

doi:10.1007/s11802-024-5590-4

J. Ocean Univ. China

2024

DOI: 10.1007/s11802-024-5590-4

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Auto-Detection Method Using Convolution Neural Network for Bottom-Simulating Reflectors

Haowei Xu,

Junhui Xing,

Boxue Yang

et al.

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Cited by 2 publications

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Optimization study of anomaly detection algorithm in machine vision inspection technology

Zhou,

Wang,

Zhuang

2024

Applied Mathematics and Nonlinear Sciences

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In recent years, target detection algorithms based on machine vision have been a hotspot in computer vision research. The You Only Look Once (YOLO) algorithm, as an excellent target detection algorithm, has played an important role in improving detection speed and accuracy with the improvement of the network architecture in its development process. This paper introduces the concept of integrated learning to the YOLOv5 network architecture, incorporating deformable convolution and attention mechanisms. It also chooses the focal EIOU loss function to replace the GIOU loss function, thereby addressing the issue of localization loss, prioritizing abnormally detected targets, and enhancing the detection efficiency of these abnormal targets. Finally, we examine the practical value of the improved YOLOv5 algorithm by testing its performance and applying it to real-world anomaly detection. The results show that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of performance and practical application advantages. In terms of performance, the classification accuracy of sea_person and earth_person in the improved YOLOv5 model is 37% and 26%, respectively, which is a significant performance gain overall. In actual application tests, the proposed method is more accurate than the traditional method. The accuracy is significantly higher.

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Optimization study of anomaly detection algorithm in machine vision inspection technology

Zhou,

Wang,

Zhuang

2024

Applied Mathematics and Nonlinear Sciences

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A study on the application of neural style transformation technique in personalized art creation

Zhao

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, using the loss function of image stylization, combined with the development of image stylization based on deep learning, the proposed convolutional neural network in the style of image conversion fitting and overfitting response. Based on this, the multi-scale feature fusion method is chosen to train the style conversion network, with the help of deep feature extraction of the image for style conversion, reconstruct the multi-scale feature fusion image, and send it to the decoder for deep coding to realize the style conversion. To evaluate the effectiveness of the proposed multi-scale feature fusion style conversion algorithm, the content loss and style loss parameters of the algorithm are analyzed using the lightweight encoder and VGG encoder, respectively. Calculate the number of algorithmic model parameters and the amount of computation. Analyze the change in the iteration number of the personalized art style conversion process and select the performance evaluation index to evaluate the results of the personalized art style image conversion process. In the process of personalized art style conversion, the multi-scale feature fusion algorithm network proposed in this paper can basically reconstruct the original image after 1000 iterations. Personalized style image reconstruction has a PSNR of 25.26 dB when the number of iterations is 1000. With the deepening of training, the reconstruction effect becomes better and better, and the advantages of personalized art style image conversion applications are significant.

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Auto-Detection Method Using Convolution Neural Network for Bottom-Simulating Reflectors

Cited by 2 publications

References 36 publications

Optimization study of anomaly detection algorithm in machine vision inspection technology

Optimization study of anomaly detection algorithm in machine vision inspection technology

A study on the application of neural style transformation technique in personalized art creation

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