Point-wise Label Confidence Map for Neural Network Object Detection
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Solution Overview
Problem
Current machine learning approaches for object detection require large datasets with consistent and accurate class labels, which are costly and challenging to obtain, especially when using deep neural networks, as they often rely on crowd-sourced or approximate labels, leading to noisy training data that degrades classification performance.
Innovation Solution
A computer architecture that uses point-wise labels by generating a confidence map for each pixel in an image based on the geometry of the target, allowing for the training of an artificial neural network to identify targets in visual data, thereby reducing the need for extensive and expensive labeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are used to improve classification performance, then classification accuracy is improved, but the size of training sets required increases dramatically
Solution Approach 1:
The patent introduces an intermediary process that generates synthetic training data through geometric transformations and noise simulations. This intermediary synthetic data acts as a bridge between limited real labeled data and the large training sets required by deep neural networks, reducing the dependency on massive real-world annotated datasets while maintaining classification performance.
Solution Approach 2:
The patent applies preliminary data preparation and augmentation techniques before actual training. By pre-generating synthetic training examples with varied geometries, noise patterns, and transformations, the system prepares a expanded training corpus in advance, reducing the need to collect and annotate large volumes of real data during the project timeline.
2Quantity of substance
If crowd-sourced labeling is used to obtain class labels, then labeling volume is increased, but labeling consistency and quality control become challenging and costly
Solution Approach 1:
The patent enables the system to generate its own training data through automated synthetic data generation. Instead of relying on external crowd-sourced labelers, the system self-generates labeled training examples by applying geometric transformations and noise simulations to existing data, eliminating the need for costly and inconsistent human labeling while maintaining data quality through controlled generation processes.
3Ease of manufacture
If automation or data mining is used to obtain labels, then labeling cost is reduced, but label accuracy becomes approximate and noisy
Solution Approach 1:
The patent transforms the quality and characteristics of training data by applying various parameter changes including geometric transformations, noise simulations, and density variations. These parameter changes create diverse synthetic training examples that maintain accuracy while reducing dependency on expensive precise labeling, as the synthetic data can be generated with controlled and known properties.
Data Source
AI summary
A neural network apparatus includes processing circuitry and memory. The memory stores a plurality of images of a target. The processing circuitry is configured to: access, from the memory, a first image and an identification of a centroid pixel of the target within the first image; generate, based on a geometry of the target and the centroid pixel, a confidence map indicating, for each pixel in the first image, a confidence value that the pixel includes the target; train, using the plurality of images of the target, including the first image and the confidence map, an artificial neural network to identify the target in visual data; and provide an output representing the trained artificial neural network.