KEYWORDS: Education and training, Data modeling, Image quality, Roads, RGB color model, Detection and tracking algorithms, Deep learning, Convolution, Network architectures, Image processing
In order to improve the image quality of a specific class of crack images, as well as to solve the problems of insufficient size of the number of crack datasets and small number of complex crack images, a crack image generation model based on DCGAN (Deep Convolutional Generative Adversarial Network, DCGAN) is proposed, which has superior training stability and convergence speed. The experimental results show that DCGAN can generate a large number of real crack images with complex backgrounds more reliably than traditional image augmentation methods, effectively solving the problem of lack of crack images in special cases and greatly reducing the cost of crack image acquisition tasks.
To overcome the limitations of deep learning models that can only capture single temporal feature: First. Concentrating solely on the abstract time series features discovered over the entire dataset, rather than the local periodic segments that contribute more to prediction accuracy; Second, only the time-series variation pattern is fitted, while other non-time series periodic patterns in the PM2.5 data set are omitted, making it impossible to increase the forecast accuracy further. A Bi-directional long and short-term memory-convolutional neural network model including the Attention Mechanism is proposed as BiLSTM-Attention-CNN. The temporal characteristics acquired by BiLSTM filtered by attention mechanism are fused with the cross-periodic features retrieved by 1DCNN to generate an ordered complementary but non-interfering feature. The final experiments revealed that, when compared to other mainstream models, the model reduces error and improves prediction accuracy, and performs well in the long-period time series prediction problem.
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