Image Processing Method Using Segmented Channel and Spatial Attention Modules
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Solution Overview
Problem
Existing attention modules in deep learning, such as the Convolution Block Attention Module (CBAM), face challenges in compatibility with objects of different sizes and distances, leading to inaccurate weight maps and high computational complexity.
Innovation Solution
The proposed image processing method involves pre-processing an image to obtain an input feature map, performing multi-channel processing to generate a channel attention feature map, processing spatial domain information to obtain a spatial attention weight, and determining an output feature map using both weights, which enhances feature extraction and reduces computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing attention modules (such as CBAM) are used, then the processing efficiency is improved to some extent, but the compatibility with objects of different sizes and distances deteriorates, resulting in inaccurate weight maps
Solution Approach 1:
The attention module is divided into two independent sub-modules: channel attention module and spatial attention module. The channel attention module processes channel dimension information to generate channel attention weights, while the spatial attention module processes spatial domain information to generate spatial attention weights. This segmentation allows each module to specialize in specific aspects, improving overall accuracy and compatibility with objects of different sizes and distances.
Solution Approach 2:
The patent introduces a new dimension of analysis by processing both channel dimension information and spatial domain information separately before combining them. The channel attention module operates on the channel dimension to capture semantic information, while the spatial attention module operates on the spatial dimension to capture positional information. This multi-dimensional approach enables the system to handle objects of various sizes and distances more effectively.
2Device complexity
If existing attention modules are used, then processing is simplified, but computational complexity increases
Solution Approach 1:
The attention module is segmented into two independent sub-modules with distinct functions. The channel attention module focuses on channel dimension processing using operations like global average pooling and convolution, while the spatial attention module focuses on spatial domain processing using similar operations. This segmentation allows for optimized computational paths and reduces redundant calculations, managing computational complexity while maintaining structural clarity.
Solution Approach 2:
The patent employs parameter changes through the use of different kernel sizes in convolution operations and adaptive pooling strategies. By adjusting these parameters dynamically based on input characteristics, the system optimizes computational complexity for different scenarios while maintaining a relatively simple overall structure.
3Measurement precision
If multi-channel processing is performed to improve feature representation, then classification precision and detection accuracy improve, but processing time increases
Solution Approach 1:
The processing is segmented into parallel operations on channel and spatial dimensions. The channel attention module and spatial attention module can be executed in parallel or with minimal sequential dependency, reducing the overall processing time compared to sequential multi-channel processing while maintaining comprehensive feature representation.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary channels and spatial regions. Through mechanisms like global average pooling and adaptive convolution, the system processes only the essential information needed for accurate classification and detection, avoiding unnecessary computational overhead while maintaining high feature representation accuracy.
Data Source
AI summary
The present application provides an image processing method, an image processing apparatus, an electronic device, and a computer-readable storage medium. The image processing method includes: pre-processing an image to be detected to obtain an input feature map; performing multi-channel processing on the input feature map to obtain a channel attention feature map; processing spatial domain information in the channel attention feature map to obtain a spatial attention weight; and determining an output feature map according to the spatial attention weight and the channel attention feature map.