With the improvement of modernization and intelligentization of dam construction sites, how to quickly identify the particle size distribution of mixed aggregates at the construction site of cemented particle dams is an urgent problem to be solved. This paper took the aggregate particle size and gradation distribution of the cemented particle dam as the research object, and developed a mixed aggregate gradation sampling detection device integrated by LED light source, industrial CCD camera and software platform, using digital image processing technology and the equivalent volume algorithm realized the digital, intelligent and automated non-contact rapid detection of aggregate gradation at the construction site. The test showed that the cumulative pass rate experimental error was below 4%, the correlation between the detected sand and gravel quality and the actual quality reached 0.96, and the gradation curve trend of the test results was consistent with the actual measured trend. It not only proved the reliability and effectiveness of the mixed aggregate gradation sampling detection device developed in this paper to quickly identify the particle size distribution of mixed aggregates in the construction environment, but also proved the accuracy and rapidity of the gradation detection method in this paper. It was of great significance to the construction of cemented particle dams in terms of economy, environmental protection and safety.
Deep learning technology is increasingly applied in vehicle license plate recognition. However, when training the model, there is a lack of data under different environments. To address this problem, several different Generative adversarial networks were applied to generate more vehicle license plate data in different environments, including low light environment, fuzzy environment, environment of bad shooting angles and environment of license plate fouling etc. Results showed that generated license plate data by CycleGAN in different environments had a good performance, which closed to real data in style migration. Wasserstein GAN (WGAN) not only the greater stability and high generalization can be achieved, but also the realistic images were produced. Deep Convolution Generative Adversarial Network (DCGAN) also generated real images but it was difficult to train. Generative adversarial networks (GAN) often had the problem of model collapse, so the ideal images cannot be generated. The better confrontation network selected in a more complex environment to extend the data set preprocessing work has great significance to improve the recognition rate of vehicle license plate recognition technology through this research.
In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.
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