Parallel Object and ROI Detection Using Shared Feature Maps
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Solution Overview
Problem
Current object detection methods, particularly in images captured by autonomous vehicles or cameras, face challenges in efficiently detecting objects such as people, especially when only a portion of the person, like a face, is visible or hidden, as they often require sequential processing that increases operational complexity and reduces efficiency.
Innovation Solution
A processor-implemented image processing method using ShuffleNet V2 to generate feature maps for parallel object and region-of-interest (ROI) classification and detection, allowing for simultaneous detection of objects and ROIs, including faces, within an input image, and displaying indicators for both detected and undetected object regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential processing is used for object detection and ROI detection, then detection accuracy is maintained, but operational complexity increases and processing efficiency decreases
Solution Approach 1:
The patent combines object detection and ROI detection into a single parallel processing framework. Both detection tasks are performed simultaneously using the same feature map generated from the input image, eliminating the need for sequential processing and reducing operational complexity while improving processing efficiency.
Solution Approach 2:
The patent segments the detection process into independent parallel streams: one stream for object detection and another for ROI detection. Each stream processes the feature map independently to produce its results, allowing simultaneous execution without interference and reducing the overall operational complexity.
2Loss of time
If sequential processing is used for object detection and ROI detection, then processing steps are simplified, but processing time increases
Solution Approach 1:
The patent merges object detection and ROI detection into a unified parallel processing system that operates simultaneously on the same feature map. This approach reduces total processing time by eliminating the sequential wait time while maintaining both detection functions, thereby improving overall processing efficiency.
Solution Approach 2:
The patent performs preliminary feature extraction to generate a comprehensive feature map before initiating parallel detection streams. This preliminary action prepares all necessary features in advance, allowing both object and ROI detection to proceed simultaneously without delays, thus reducing processing time and improving efficiency.
3Productivity
If parallel processing is used for object and ROI detection, then processing efficiency improves, but computational resources increase
Solution Approach 1:
The patent merges object detection and ROI detection by having both processes share the same pre-generated feature map. This sharing of computational resources allows parallel processing to improve efficiency while avoiding the redundant computation that would otherwise occur if separate feature extraction processes were used, thus mitigating the increase in computational resource requirements.
Solution Approach 2:
The feature map generation process serves multiple functions: it provides the basis for both object detection and ROI detection simultaneously. This multi-functionality allows the system to perform parallel detection tasks using a single feature representation, improving processing efficiency while keeping computational resource usage manageable.
Data Source
AI summary
An electronic device generates a feature map from an input image to perform object detection, classifies one or more objects included in the input image and determines one or more object regions including the one or more objects based on the feature map, classifies an ROI included in at least a portion of the objects and determines the ROI included in the input image based on the feature map, displays on the input image an indicator identifying a first object region of a first object where the ROI is determined and a feature point of a first ROI of the first object, and displays on the input image an indicator identifying a second object region of a second object where the ROI is not determined and a feature point of the second object region, to perform post-processing differently according to whether an ROI is determined in an object.