Image Segmentation with Different Scale Convolutional Neural Networks

Image segmentation is a crucial task in computer vision and image processing. It is widely used in many necessary fields, such as scene understanding, medical image analysis, robot perception, video surveillance, augmented reality, and image compression. Numerous algorithms for image segmentation have been proposed, demonstrating their unique advantages and limitations in their respective application scenarios. In image processing and pattern recognition, the importance and criticality of image segmentation are self-evident. Its core task is to divide the entire image into several regions with specific meaning and define a category label for each area. In recent years, convolutional neural networks (CNN) have performed well in image segmentation and have become one of the most popular and widely used models. This paper focuses on changing the model scale, which significantly impacts the segmentation results by changing the size of the data set used to train the model. This paper aims to explore the impact of data volume on model performance. For example, will the segmentation results become more accurate as the model scale increases? This paper first created and trained a CNN model using different scales. In each training, this paper trains the model for 50 epochs, which can significantly improve the reliability and accuracy of the experimental results. Next, this paper segments the test image, analyzes the segmentation effect, and further explores the relationship between parameters scale and model performance. This research will provide new ideas and references for optimizing image segmentation.