Circular-Softmax Block for Polygon Localization
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Solution Overview
Problem
Deep learning pipelines struggle to accurately localize polygons in images, especially when they are partially occluded, due to limitations in their internal architecture.
Innovation Solution
Incorporating a circular-softmax block within the deep learning pipeline, which includes a soft-argmax layer and a circular-mean layer, that normalizes Hough heatmaps and applies a threshold-triggered approach to determine the appropriate function for polygon localization, thereby enhancing accuracy in occluded scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning pipelines are used for polygon localization, then the system architecture remains simple, but localization accuracy deteriorates under partial occlusion
Solution Approach 1:
The circular-softmax block segments the localization task into distinct functional layers: soft-argmax layer for initial parameter estimation, circular-mean layer for refined parameter calculation, and variance check for quality control. Each layer processes Hough heatmap data independently but contributes to the final localization result, enabling accurate polygon detection even under occlusion while maintaining modular architecture
Solution Approach 2:
The circular-softmax block acts as an intermediary component between the backbone network and the final localization output. It introduces intermediate processing steps (soft-argmax, circular-mean, variance checking) that refine the raw Hough heatmap data before producing final polygon parameters, thereby improving accuracy without requiring fundamental changes to the overall pipeline architecture
2Reliability
If the deep learning pipeline uses a simple architecture, then the device complexity is low, but it fails to maintain accuracy when polygons are partially occluded
Solution Approach 1:
The circular-softmax block implements dynamic processing by conditionally selecting between different computational paths based on variance thresholds. The variance check layer dynamically determines whether to trust the soft-argmax estimates or to rely more heavily on circular-mean calculations, allowing the system to adapt its processing strategy based on the quality of intermediate results and the degree of occlusion detected
Solution Approach 2:
The circular-softmax block changes key parameters of the localization process by introducing circular statistics (circular-mean, circular-softmax) instead of conventional linear statistics. This parameter transformation enables the system to handle wraparound effects in angular measurements and maintain accuracy under occlusion by properly modeling the periodic nature of orientation data
3Measurement precision
If conventional localization methods are used, then the processing speed is fast, but measurement precision deteriorates in occluded scenarios
Solution Approach 1:
The soft-argmax layer performs preliminary action by providing initial estimates of polygon edge parameters before the final localization decision is made. These preliminary estimates serve as a foundation that can be refined by the circular-mean layer or used directly if variance checks confirm sufficient accuracy, enabling a multi-stage refinement process that improves precision without requiring all components to execute in every case
Data Source
AI summary
An example device is described for facilitating polygon localization. In various aspects, the device can comprise a processor. In various instances, the device can comprise a non-transitory machine-readable memory that can store machine-readable instructions. In various cases, the processor can execute the machine-readable instructions, which can cause the processor to localize a polygon depicted in an image, based on execution of a deep learning pipeline. In various aspects, the deep learning pipeline can comprise a circular-softmax block.