Image Classification Method Based on Multi-Scale Convolutional Neural Network

Traditional convolutional neural networks (CNNs) typically use fixed scale convolutional kernels for feature extraction when processing image classification tasks, while ignoring the multi-scale information present in the image. To overcome this limitation, we propose an algorithm based on multi-scale CNNs, which capture features at different levels by introducing convolutional kernels of different scales into the convolutional layer. In this study, we first designed a multi-scale convolutional layer consisting of multiple convolutional kernels of different scales to extract multi-scale features of the image. To further enhance classification performance, we introduced a multi-scale feature fusion module that can effectively fuse features of different scales and classify them through a fully connected layer. Then we conducted extensive experiments on several commonly used image classification datasets. The experimental results show that this network can not only effectively identify and locate hyperspectral image targets in different scenarios, but also reduce missed detections and false positives during the detection process. The average accuracy of the improved model has been improved, and the recognition accuracy of some small markers affected by external factors such as occlusion and lighting has also been improved. In addition, by comparing the detection effect of a single image, the progressiveness and anti-leakage ability of the improved model are proved. The image classification method based on multi-scale CNNs has broad application prospects in image recognition and feature extraction, and can provide valuable reference and reference for research in related fields.