R-CNN ROI Pooling Aspect Ratio Segmentation
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Solution Overview
Problem
Region-based convolutional neural networks (R-CNNs) face issues with preserving the aspect ratio of regions of interest (ROIs) during pooling, leading to potential loss of recognition accuracy and increased computational overload due to unnecessary operations.
Innovation Solution
The proposed method involves generating a feature map from an input image, detecting ROIs, classifying them into groups based on aspect ratios, performing pooling with divided regions set according to representative aspect ratios, and applying an image classifier after regression operations, ensuring that the aspect ratio information is maintained and computational resources are optimized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pooling is performed without considering aspect ratio of ROIs, then processing speed is improved, but aspect ratio information is lost and recognition accuracy degrades
Solution Approach 1:
The patent segments ROIs into multiple groups based on their aspect ratios, creating distinct processing pathways for different aspect ratio ranges. This segmentation allows the system to maintain aspect ratio information by applying appropriate pooling operations tailored to each group, rather than using a single uniform pooling approach that would lose aspect ratio details.
2Manufacturing precision
If detection network is applied to each preset proportion in ROI, then aspect ratio information loss is reduced, but computational resources increase causing overload
Solution Approach 1:
Instead of applying detection networks to all possible preset proportions, the patent applies detection networks only to representative aspect ratios for each group. This partial action approach maintains aspect ratio information for critical cases while avoiding the computational overload that would result from processing every possible proportion, thus achieving a balance between accuracy and efficiency.
3Manufacturing precision
If ROIs are classified into groups based on aspect ratio, then recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter used for grouping from individual aspect ratio values to discrete aspect ratio ranges or bins. This parameter transformation simplifies the classification process by reducing the continuous aspect ratio spectrum into manageable groups, thereby improving recognition accuracy through aspect ratio-aware processing while minimizing the increase in system complexity.
Data Source
AI summary
An image recognition method using a region-based convolutional neural network (R-CNN) includes generating a feature map from an input image, detecting one or more regions of interest (ROIs) in the feature map, classifying the ROIs into groups based on setting information, performing pooling on the ROIs classified into the groups independently for each of the groups, and performing a regression operation on a result of the pooling and applying an image classifier to a result of the regression operation.