Research on Image Processing of Big Data Based on Computer Vision

With the advent of the era of big data, the image processing of big data based on Computer Vision (CV) has become a research field of great concern. The purpose of this study is to explore how to improve image processing methods through deep learning technology to better adapt to large-scale and high-dimensional image data. We propose an innovative image processing framework, based on the improved ResNet50, and introduce multi-scale feature fusion, attention mechanism and width adaptation strategies. In the task of image classification, our method has achieved remarkable improvement in accuracy, precision, recall and FI value. Through the effective fusion of multi-scale features, the model better captures the abstract information in the image and improves the classification accuracy. In the target detection task, our method shows excellent performance in mAP, false alarm rate and so on. The improved ResNet50, which introduces attention mechanism, is more reliable in identifying the target position and category. The experimental results further prove the innovation and practicability of our proposed method. Compared with the traditional model and the latest image processing methods, our method shows significant advantages in big data image processing. This provides new ideas and technical support for solving large-scale and diversified image data processing problems. The contribution of this study is to provide a new solution for big data image processing based on CV. The improved ResNet50 model has made remarkable progress in performance, which opens up a new research direction for deep learning research in the field of big data image processing.