Weakly-Supervised Semantic Segmentation for Small Cell Site Selection
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Solution Overview
Problem
Current methods for small cell site selection are inefficient due to the need for manual pixel-wise annotations, which are time-consuming and costly, and lack accurate localization and segmentation, especially in outdoor scenes where visual inspection is required for suitable hosting structures like poles and buildings.
Innovation Solution
A weakly-supervised learning method combining active learning and pseudo-labeling techniques to train a semantic segmenter that can classify and segment regions of interest in images with reduced pixel-wise annotations, using a deep convolutional architecture and pseudo-labeling to improve segmentation accuracy and reduce annotation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel-wise annotations are used for training, then segmentation accuracy can be improved, but annotation time and cost increase significantly
Solution Approach 1:
The system employs self-training where the model generates its own annotations through pseudo-labeling. The model annotates unlabeled images, and these self-generated annotations are used to retrain and improve the model, creating a self-improving cycle that reduces dependency on manual annotations while maintaining segmentation accuracy
Solution Approach 2:
The system performs preliminary automated annotation using the model before manual verification. By pre-annotating images with the trained model and only requiring manual correction or verification of uncertain cases, the overall annotation time is significantly reduced while maintaining high accuracy
2Reliability
If more pixel-wise annotations are collected, then model performance improves, but annotation costs increase
Solution Approach 1:
The system uses self-training with pseudo-labeling where the model generates its own training data. By iteratively annotating unlabeled images and retraining on the expanded dataset, the model achieves improved performance without proportional increases in manual annotation costs
Solution Approach 2:
The system implements a feedback loop where model predictions are evaluated, and high-confidence predictions are fed back into the training set as pseudo-labels. This continuous feedback mechanism allows the model to improve performance using primarily automated annotations with minimal manual intervention
3Measurement precision
If visual inspection is required for each location, then installation suitability can be accurately determined, but the process becomes time-consuming
Solution Approach 1:
The system replaces manual visual inspection with an automated deep learning model that processes images to identify suitable small cell hosting structures. The model automatically detects and classifies potential installation sites, substituting the mechanical visual inspection process with automated image analysis while maintaining accuracy
Solution Approach 2:
The system creates a digital representation of physical locations through image processing and automated annotation. By working with annotated image copies rather than requiring physical inspection of each site, the system maintains assessment accuracy while dramatically improving site selection efficiency
Data Source
AI summary
The disclosure relates to a method for training a model for labeling images. The method comprises obtaining a first dataset of labeled images, the first dataset of labeled images being selected from a pool of images presenting different views of portions of geographical areas; selecting, from the pool of images, a second dataset of images for pseudo labeling, different from the first dataset; feeding the second dataset of images into the model and obtaining as output of the model the second dataset of images with pseudo-labels; combining the first dataset of labeled images with the second dataset of images with pseudo-labels into a third dataset; training the model with the third dataset; and testing the model with a fourth dataset of labeled images and, upon determining that a requested performance is met, storing the model, or, upon determining that the requested performance is not met, executing the method again.