Road Defect Detection Model Using Pseudo-Labels
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Solution Overview
Problem
Current infrastructure maintenance relies heavily on reactive approaches, which are inefficient as they require prior detection of defects, and struggle to effectively monitor road conditions in real-time, especially in adverse conditions.
Innovation Solution
A machine learning-based system that uses a road defect detection model trained with pseudo-labels, combining data from visual and LiDAR sensors to identify and annotate road defects, allowing for proactive maintenance and hazard avoidance by self-driving vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reactive maintenance is used based on defect reporting or discovery, then maintenance can be performed on actual problems, but maintenance efficiency is low and cannot occur until information about the defect has been collected
Solution Approach 1:
The system performs preliminary detection of road defects using machine learning models that analyze images and LiDAR data before defects become critical hazards. By proactively identifying potential issues through automated analysis of training datasets with pseudo-labels, the system enables maintenance activities to be scheduled in advance, eliminating the time delay inherent in reactive maintenance approaches where defects are only detected after they manifest as reportable problems.
2Productivity
If manual defect detection is used, then detailed inspection can be performed, but the process is labor-intensive and cannot monitor road conditions in real-time
Solution Approach 1:
The system replaces manual mechanical inspection processes with automated machine learning-based detection systems. The model processes images and LiDAR sensor data to automatically identify road defects, substituting human labor with computational algorithms that can analyze multiple data sources simultaneously and continuously monitor road conditions in real-time without the limitations of manual inspection.
3Measurement precision
If comprehensive training data with manual annotations is used, then model accuracy is improved, but the training process is time-consuming and resource-intensive
Solution Approach 1:
The system implements self-service annotation through pseudo-labeling, where the machine learning model automatically generates labels for training data without requiring extensive manual annotation. The model processes unlabeled images and LiDAR data, generates predicted labels, and uses these pseudo-labels to train and refine itself iteratively. This self-annotating capability significantly reduces the time and resources required for data preparation while maintaining high detection accuracy.
Data Source
AI summary
Methods and systems for training a model include annotating a subset of an unlabeled training dataset, that includes images of road scenes, with labels. A road defect detection model is iteratively trained, including adding pseudo-labels to a remainder of examples from the unlabeled training dataset and training the road defect detection model based on the labels and the pseudo-labels.