Compact Neural Network for Real-Time Keypoint Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models for object detection and image processing are typically large and resource-intensive, making them unsuitable for devices with limited hardware resources, such as smartphones, and struggle to provide real-time results in applications like augmented reality.
Innovation Solution
A compact machine-learning model with an optimized architecture that includes a backbone neural network, region proposal network, detection head, keypoint head, and segmentation head, using techniques like RoIAlign for accurate feature extraction and interpolation to refine keypoint locations, enabling efficient real-time object detection and segmentation on limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large machine-learning model is used for object detection and image processing, then detection accuracy and processing capability are improved, but computational resources and processing time increase significantly
Solution Approach 1:
The machine-learning model is divided into separate modules: a backbone neural network for feature extraction, a region proposal network for identifying potential objects, and detection heads for specific tasks. This segmentation allows each component to be optimized independently and enables processing on devices with limited computational resources while maintaining detection accuracy.
Solution Approach 2:
The patent applies parameter changes by using RoIAlign with specific spatial alignment parameters and adjusting the architecture parameters of the neural network layers. These parameter optimizations enable the model to achieve high detection accuracy while reducing computational requirements for deployment on mobile devices.
2Reliability
If a complex machine-learning model is deployed on mobile devices with limited hardware resources, then detection capability is improved, but real-time processing is not achieved
Solution Approach 1:
By segmenting the model into a backbone network and separate detection heads, the system can process images more efficiently on mobile devices. The region proposal network filters out unnecessary areas, allowing the detection heads to focus computational resources only on relevant regions, thereby achieving real-time processing speeds.
Solution Approach 2:
The system performs partial action by processing only the most relevant regions of an image identified by the region proposal network, rather than processing the entire image with full computational complexity. This selective processing enables real-time detection on devices with limited hardware resources.
3Productivity
If standard keypoint detection methods are used, then object detection is performed, but precision in keypoint localization is insufficient
Solution Approach 1:
The region proposal network performs preliminary action by identifying and selecting the most likely regions containing objects before the detection heads process them. This preliminary filtering maintains high detection throughput while enabling precise keypoint localization in the subsequent processing stages.
Solution Approach 2:
The detection heads apply local quality by focusing computational processing on specific regions of interest identified by the region proposal network. This allows high-precision keypoint localization to be achieved only where needed, maintaining overall detection efficiency while improving localization accuracy in critical areas.
Data Source
AI summary
In one embodiment, a system may access first, second, and third probability models that are respectively associated with predetermined first and second body parts and a predetermined segment connecting the first and second body parts. Each model includes probability values associated with regions in an image, with each value representing the probability of the associated region containing the associated body part or segment. The system may select a first and second region based on the first probability model and a third region based on the second probability model. Based on the third probability model, the system may compute a first probability score for regions connecting the first and third regions and a second probability score for regions connecting the second and third regions. Based on the first and second probability scores, the system may select the first region to indicate where the predetermined first body part appears in the image.