Residual Network Feature Extraction with Variable Kernel Stride
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Solution Overview
Problem
Current image processing methods using neural networks, such as ResNet, face challenges in accurately extracting image features, which affects the accuracy of image recognition.
Innovation Solution
The proposed method employs a residual network with specifically configured residual blocks, including a convolution branch and a residual branch, where the size of the convolution kernel of the first convolutional layer is less than that of the second layer, and the convolution stride of the second layer is greater than the first, ensuring no feature points are skipped during downsampling, thereby improving feature extraction and recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a residual network with conventional convolutional layers is used for feature extraction, then the network structure is simple and easy to implement, but the image feature extraction accuracy is insufficient
Solution Approach 1:
The convolutional layer is segmented into two distinct parts: a first convolutional layer with a first convolution kernel and a second convolutional layer with a second convolution kernel. This segmentation allows each layer to perform different functions - the first layer extracts basic features while the second layer captures more complex patterns, thereby improving overall feature extraction accuracy without significantly increasing implementation difficulty
Solution Approach 2:
Different convolution kernels are applied at different stages of the convolutional layer. The first convolutional layer uses a first convolution kernel optimized for initial feature extraction, while the second convolutional layer uses a second convolution kernel optimized for deeper feature capture. This local differentiation of kernel properties enhances the quality of feature extraction at each stage
2Productivity
If downsampling is performed during feature extraction, then the computational complexity is reduced, but feature point information may be lost
Solution Approach 1:
A pooling layer is introduced as an intermediary component between the convolutional layers. This pooling layer performs downsampling in a controlled manner, reducing the spatial dimensions of feature maps and thereby lowering computational complexity for subsequent layers, while the surrounding convolutional operations ensure that critical feature point information is preserved through the downsampling process
Data Source
AI summary
An image processing method includes: obtaining a target image; performing feature extraction on the target image based on a residual network, to obtain image feature information; and performing recognition processing on the target image according to the image feature information. The residual network includes a plurality of residual blocks that are successively connected, each of the residual blocks including a convolution branch and a residual branch, a size of a convolution kernel of a first convolutional layer in the convolution branch being less than a size of a convolution kernel of a second convolutional layer located after the first convolutional layer, and a convolution stride of the second convolutional layer being greater than a convolution stride of the first convolutional layer and less than a width of the convolution kernel of the second convolutional layer.