Local Texture Feature Fusion for Object Location Detection
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Solution Overview
Problem
Current image processing technologies face challenges in accurately detecting the location of target objects within images due to the lack of effective fusion of features from different channels, which limits the richness and accuracy of image characteristics representation.
Innovation Solution
The method involves acquiring a feature map of a target image, determining a local feature map of a target size, combining features of different channels to obtain a local texture feature map, and using a pre-trained deep neural network to obtain location information, where the features are fused using a combined processing layer to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If features of different channels are not fused effectively, then the device complexity is reduced, but the measurement precision of target object location deteriorates
Solution Approach 1:
The patent merges features from multiple channels (spatial, spectral, temporal) into a unified feature representation. The combined processing layer integrates these diverse feature channels through systematic fusion operations, creating a comprehensive feature set that improves location detection accuracy while maintaining manageable system complexity through structured combination strategies.
Solution Approach 2:
The patent creates composite feature representations by combining features from different channels analogous to composite materials. The combined processing layer synthesizes spatial, spectral, and temporal features into a composite feature structure that leverages the strengths of each individual channel, achieving superior measurement precision through diversified feature composition.
2Reliability
If features of different channels are combined to enrich image characteristics, then the detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the feature fusion process into distinct functional components within the combined processing layer. By dividing the complex feature combination task into manageable segments (spatial feature processing, spectral feature processing, temporal feature processing), the system achieves reliable detection accuracy while controlling processing layer complexity through modular organization.
Solution Approach 2:
The patent addresses complexity by transitioning to a higher-dimensional processing space where multiple channel features are integrated. The combined processing layer operates in this extended dimensional space, combining features across spatial, spectral, and temporal dimensions simultaneously, which improves detection reliability while managing complexity through dimensional organization rather than sequential processing.
3Productivity
If local feature maps are used instead of full feature maps, then the processing efficiency is improved, but the loss of information increases
Solution Approach 1:
The patent applies local quality by processing different regions of the feature map with appropriate attention. The combined processing layer focuses computational resources on locally significant regions while maintaining essential global context, thereby improving processing efficiency without excessive information loss. This selective local processing allows the system to prioritize computationally intensive operations where they provide maximum value.
Solution Approach 2:
The patent implements partial action by applying the combined processing layer selectively to local feature maps rather than processing the entire feature map uniformly. This approach processes only the most relevant portions of the feature space in detail, achieving improved processing efficiency while maintaining adequate detection accuracy through strategic selection of processing regions.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for processing an image. A specific embodiment of the method includes: acquiring a feature map of a target image, where the target image contains a target object; determining a local feature map of a target size in the feature map; combining features of different channels in the local feature map to obtain a local texture feature map; and obtaining location information of the target object based on the local texture feature map.