Object Detection via Selective Pooling for Overlapping Features
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Solution Overview
Problem
Existing object detection methods face challenges in efficiently extracting features based on object locations, leading to suboptimal performance in identifying and classifying objects, especially when objects overlap.
Innovation Solution
The proposed solution involves a two-stage object detection framework that uses a Convolutional Neural Network (CNN) to convert images into feature maps, followed by a Region Proposal Network (RPN) to generate regions of interest. A selective pooling layer is applied to the feature bank to extract target feature maps, which are then used to determine object classes and boundaries through a fully connected layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used, then object detection can be performed, but detection performance and accuracy are suboptimal, especially for overlapping objects
Solution Approach 1:
The patent divides the feature map into multiple channels, each channel processing specific spatial regions or feature types independently. This segmentation allows parallel processing of different object regions, improving both detection accuracy for overlapping objects and computational efficiency through channel-wise parallelization
Solution Approach 2:
The patent applies different processing strategies to different spatial regions and channels based on their specific characteristics. Each channel can be optimized for specific types of objects or regions, allowing local optimization that improves overall detection performance while maintaining computational efficiency
2Productivity
If feature map dimensionality is reduced, then computational efficiency improves, but feature information may be lost
Solution Approach 1:
The patent transforms the feature representation by organizing features into multiple channels, effectively changing the dimensional organization from a single large feature map to a structured multi-channel format. This dimensional reorganization reduces computational complexity while preserving feature information through the channel structure that maintains spatial and semantic relationships
Data Source
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AI summary
Implementations of the subject matter described herein relate to object detection based on deep neural network. With a given input image, it is desired to determine a class and a boundary of one or more objects within the input image. Specifically, a plurality of channel groups is generated from a feature map of an image, the image including at least a region corresponding to a first grid. A target feature map is extracted from at least one of the plurality of channel groups associated with a cell of the first grid. Information related to an object within the region is determined based on the target feature map. The information related to the object may be a class and/or a boundary of the object.