Abstract
The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images’ quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model’s performance.
Highlights
Researches regarding data cleaning are first appeared in the United States, on the correction of social security number errors
The entire structure of the DeBlurGAN training network for motion blur removal is shown in Figure 5, where the generator network takes the blur image as input and produces the reconstructed image
It is considered that the metro pedestrian dataset with large data volume and low data quality is the main reason for the poor performance of pedestrian detection model
Summary
Researches regarding data cleaning are first appeared in the United States, on the correction of social security number errors. We identify the quality problems existing in the dataset and summarize information regarding data’s quality (3) Determination of cleaning rules. Wireless Communications and Mobile Computing (4) Cleaning and correction This part involves cleaning the data according to the defined cleaning rules, using related technologies to correct the dirty data, and meeting the requirements of demand analysis. The variance of the image is calculated according to the Laplace operator, the degree of blur of the image is identified, and the distribution of fuzzy image and clear image in the collected image is statistically analyzed (3) Set cleaning rules. The dataset obtained by using different data cleaning rules will be sent into the classical YOLOV3 network to test the performance of the model and analyze the effectiveness of the data cleaning method used in this paper in the pedestrian detection task. The fourth section is the experimental design and results, and the fifth section is the conclusion of this paper
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