Multi-Core Object Detection Parallel Processing

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Solution Overview

Problem

Conventional face detection techniques are resource-intensive, making them unsuitable for mobile devices with limited processing and memory resources, leading to performance issues, real-time limitations, and rapid battery drainage.

Innovation Solution

The implementation of object detection techniques that utilize a multi-core processor system to analyze images in parallel, employing different scales, angles, and regions, and leveraging previous detection data to optimize the detection process, thereby reducing computational resource consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional face detection techniques are used, then detection accuracy is maintained, but computational resources are excessively consumed

Engineering Contradiction:

Improvedetection accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments the face detection task by dividing the image into multiple regions and analyzing them at different scales using separate processor cores. Each core handles specific portions of the image at different resolutions, allowing the system to maintain detection accuracy while reducing overall computational resource consumption by parallelizing the workload.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a multi-scale dimension to the detection process by analyzing images at different resolutions and orientations simultaneously. This dimensional approach allows the system to detect faces at various sizes and angles without requiring all processor cores to process the entire image at full resolution, thereby reducing computational burden while maintaining accuracy.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Reliability

If conventional face detection techniques are used, then detection capability is provided, but real-time performance is not achieved

Engineering Contradiction:

Improvedetection capabilityVSAvoidreal-time performance

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

By segmenting the detection task across multiple processor cores, each core can independently process specific image regions or scales without blocking others. This parallel segmentation enables real-time performance by distributing the computational workload, allowing the system to maintain detection capability while achieving real-time processing speeds.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis by first detecting faces at lower resolutions or in preliminary image regions before performing more detailed analysis. This staged approach with preliminary action allows the system to quickly identify potential faces and then apply more computationally intensive analysis only where necessary, achieving real-time performance without sacrificing detection capability.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If conventional face detection techniques are used, then detection function is implemented, but battery power is drained rapidly

Engineering Contradiction:

Improvedetection functionVSAvoidbattery power consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent applies partial action by having processor cores analyze only specific portions of the image at different scales rather than processing the entire image at full resolution. This partial analysis approach maintains detection function by checking multiple regions and scales while significantly reducing the total computational workload and associated battery power consumption.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

Segmenting the detection function across multiple cores with specialized tasks (different scales, orientations, regions) allows the system to maintain comprehensive detection capability while distributing the energy consumption. The parallel architecture ensures that no single core is overwhelmed, and the overall power consumption is managed more efficiently compared to sequential processing of the entire image.

Inventive Principle:

Principle #1Segmentation

4Productivity

If multiple processor cores are used with different scales and angles, then computational efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements multi-functionality by designing processor cores that can be configured to perform different detection tasks (different scales, orientations, regions) while using the same basic architectural framework. This universal approach allows the system to achieve high computational efficiency through parallel processing while managing complexity by using a standardized core design that can be dynamically configured for different detection needs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS9589179B2Object detection techniques

Publication Date: 2017.03.07 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Object detection techniques are described. In one or more implementations, a plurality of images are received by a computing device. The plurality of images are analyzed by the computing device to detect whether the images include, respectively, a depiction of an object. If an object is found in a first image, the locations, angles and scales for object detection can be further restricted in a second one. If an object is not found in a first one of the image, different portions of a second one of the images are analyzed for object detection.