Abstract
With the rapid development of information technology, image recognition has become increasingly crucial in numerous fields. Deep learning, as a highly potent machine learning approach, has brought about a revolutionary breakthrough in image recognition. This paper delves into the optimization of image recognition algorithms based on deep learning. Firstly, it analyzes the application status of deep learning in image recognition, covering common neural network architectures such as convolutional neural network (CNN). Then, the optimization strategy of the image recognition algorithm is elaborated from multiple aspects, including data augmentation to increase data diversity, model structure optimization for better feature extraction, and hyperparameter adjustment to enhance performance. Through extensive experiments, the effects of different optimization methods are compared meticulously. It is demonstrated that the optimized algorithm shows significant improvements in accuracy, robustness, and efficiency. Finally, the paper looks ahead to the future development trend of image recognition algorithms based on deep learning, envisioning further advancements and broader applications.
Published Version
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