Object Detection With Environment-Invariant Feature Maps

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Solution Overview

Problem

Existing object detection systems face challenges in adapting to new domains due to distributional shifts, requiring extensive training data and computational resources, and struggle with domain-specific annotations, misalignment, and lack of category labels, leading to inefficiencies and inaccuracies in object detection across diverse environments.

Innovation Solution

A system and method that generates environment-invariant feature maps by combining feature maps with pixel-wise probability outputs, using an environment classifier to reduce domain dissimilarity, allowing for robust and accurate object detection across different environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If adversarial learning methods are used to align image features at global and local levels, then domain adaptation capability is improved, but instance-specific misalignment persists and computational complexity increases

Engineering Contradiction:

Improvedomain adaptation capabilityVSAvoidinstance-specific alignment accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent segments the feature alignment process into two distinct levels: global feature alignment (using adversarial learning on pooled features) and local feature alignment (using attention mechanisms on instance-specific features). This segmentation allows each level to address specific alignment needs independently, resolving the contradiction between overall domain adaptation and instance-specific accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an attention mechanism as an intermediary component that operates between the global and local feature representations. This attention mechanism selectively weights different regions and features, acting as a mediator that refines the global alignment at the instance level without requiring extensive retraining, thus improving instance-specific alignment while maintaining computational efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If two-stage detectors with extensive training data are used, then detection accuracy in label-rich domains is improved, but annotation cost and time consumption increase significantly

Engineering Contradiction:

Improvedetection accuracyVSAvoidannotation time consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts and utilizes unlabeled target domain images to pre-train the domain adaptation components (global and local discriminators). By taking out the annotation requirement from the target domain training process, the system achieves domain adaptation without incurring annotation costs for the target domain, while still maintaining detection accuracy through the extracted feature alignment capabilities.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent performs preliminary domain adaptation training using unlabeled target images before the actual detection task. This preliminary action pre-aligns the feature distributions between source and target domains, reducing the need for extensive annotated target data during deployment and significantly reducing annotation time consumption while preserving detection accuracy.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If existing domain adaptation frameworks are used to handle substantial differences in object appearance, then adaptability to new domains is improved, but alignment effectiveness decreases due to distributional shifts

Engineering Contradiction:

Improveadaptability to new domainsVSAvoidalignment effectiveness

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent addresses substantial appearance differences by operating in multiple feature dimensions simultaneously: global pooled feature space for overall domain alignment and local instance-level feature space for specific object alignment. This multi-dimensional approach allows the system to handle distributional shifts effectively by aligning features across different representational levels, maintaining both adaptability and alignment effectiveness.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12450878B2System and method for detecting object in an adaptive environment using a machine learning model

Publication Date: 2025.10.21 INT INST OF INFORMATION THCHNOLOGY HYDERABAD

AI summary

A method for detecting object in an image in a target environment that is adapted to a source environment using a machine learning model is provided. The method includes (i) extracting features from source image associated with source environment and target image associated with target environment, (ii) generating a feature map based on the features, (iii) generating a pixel-wise probability output map (iv) determining a first environment invariant feature map by combining the feature map with the pixel-wise probability output map, (v) determining a second environment invariant feature map by combining the first environment invariant feature map and the features, (vi) generating environment invariant feature maps at different instances, (vii) extracting environment invariant features based on the environment invariant feature maps, (viii) detecting the object in the image in the target environment that is adapted to the source environment by training the machine learning model using the environment invariant features.