Research on Image Recognition Problems Based on Deep Learning Models

Abstract

Deep learning has achieved impressive results in computer vision but typically requires extensive datasets, which are often costly and difficult to obtain, particularly in specialized domains like medical imaging and autonomous driving. To address this challenge, various image augmentation algorithms have been introduced, aimed at enhancing the size and diversity of training datasets. While model based techniques make use of picture production models, model-free techniques employ conventional image processing techniques. The goal of optimizing policy-based techniques is to enhance model performance by determining the optimal set of augmentation procedures. The seminal AlexNet model, introduced in 2012, marked a significant breakthrough in image classification, demonstrating the power of deep learning. Through advanced techniques such as multi-scale convolution, cross-connections, and global average pooling, these approaches have substantially improved image classification accuracy. Despite ongoing challenges, these advancements underscore the critical role of data augmentation and optimized neural network architectures in enhancing performance when data is limited.