The Image Recognition Using Enhanced Convolutional Neural Networks

Abstract

Image recognition remains one of the most challenging tasks in computer vision due to variations in illumination, viewpoint, scale, occlusion, and background clutter. Convolutional Neural Networks (CNNs) have become the dominant approach for this task because of their ability to automatically learn hierarchical feature representations through local receptive fields, weight sharing, and spatial subsampling. This paper presents an enhanced CNN architecture that employs smaller convolutional kernels, deeper stacking, and optimized training strategies to achieve higher accuracy with reduced computational complexity. The proposed model is evaluated on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) benchmark and compared against state-of-the-art methods. Experimental results demonstrate a Top-5 error rate of 9.18%, outperforming several contemporary approaches while maintaining scalability and training efficiency.