Occluded, small-scale pedestrians are easy to occur in dense scenes to solve this problem, an improved Yolov5 is proposed detection algorithm. First of all, feature extraction for small-size pedestrians is insufficient the problem uses SPD-Conv convolution to enhance the complex background small target feature extraction ability. Second, fuse an efficient multi-foot the degree of attention module further enhances the visual area of the target pedestrian feature extraction. Finally, focal eiou loss was used as the loss letter number, so that the regression more attention to high-quality samples, improve the accuracy of the model and robustness. Training and testing were performed on the dataset CrowdHuman in the test, the improved Yolov5 algorithm AP50 can reach 83.6 %, phase for the original algorithm, AP50 and AP50−95 are improved by 2.5 %, respectively. Average accuracy improved by 2.2%. The experimental results validate the proposed method effectiveness in crowded scenarios.
KEYWORDS: Data modeling, Pollution, Tunable filters, Random forests, Nitrogen, Education and training, Image filtering, Chemical analysis, Analytical research, Machine learning
According to the national policy of proposing water pollution prevention and control regulations for the Chaohu Lake basin, to solve the current problem of chemical pollution in the Chaohu Lake basin, the relevant environmental data of the Chaohu Lake basin provided by Chengxin Environmental Testing Company were used, and a random forest algorithm based on high correlation filtering was proposed to construct a prediction model for the concentration of chemical pollution such as ammonia nitrogen in the Chaohu Lake basin. The method uses high correlation filtering to eliminate the strong correlation between water pollution data and adjusts the parameters of the random forest model to achieve the best prediction effect for the training data. The model was tested by real data sets, and the Bayesian ridge regression model, ordinary linear regression model, elastic network regression model, support vector machine model, and correlation vector machine model were used for comparison experiments, and the average relative error was used as the evaluation index. Finally, the random forest algorithm model with high correlation filtering achieved the best prediction results in the study of quantitative analysis of eutrophic chemical pollution such as ammonia nitrogen in the Chaohu Lake basin.
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