Two-Tier Road Image Classification for Accurate, Timely Warnings
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Solution Overview
Problem
Current road monitoring systems face challenges in accurately classifying objects in large datasets collected by autonomous vehicles and infrastructure due to computational limitations at edge nodes, leading to delayed data processing and inability to identify new object categories efficiently.
Innovation Solution
A method involving a two-tiered approach where a first node performs coarse-grained classification and a second node closer to the cloud performs fine-grained classification, using neural search techniques to improve accuracy without increasing costs, by leveraging a trained classification model and feature libraries at the near edge node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If coarse-grained classification is performed at edge nodes, then processing speed is improved, but classification accuracy deteriorates
Solution Approach 1:
The classification process is segmented into two stages: coarse-grained classification at edge nodes for quick processing, and fine-grained classification at cloud nodes for high accuracy. This segmentation allows each node to perform its specialized function optimally, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The coarse-grained classification result serves as an intermediary that filters and pre-processes data before transmission to cloud nodes. This intermediary step reduces the burden on cloud nodes while maintaining overall system accuracy, enabling both fast edge processing and accurate cloud classification.
2Measurement precision
If fine-grained classification is performed at edge nodes, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
Computational tasks are segmented and distributed: edge nodes perform simple coarse-grained classification, while complex fine-grained classification is assigned to cloud nodes with sufficient computational resources. This segmentation reduces device complexity at edge nodes while maintaining high classification accuracy through cloud processing.
3Measurement precision
If all data is transmitted to cloud nodes, then classification accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The essential classification function is extracted and executed at edge nodes through coarse-grained classification. Only the results or selectively filtered data are transmitted to cloud nodes, rather than transmitting all raw data. This extraction approach maintains classification accuracy while significantly reducing bandwidth consumption.
Solution Approach 2:
Coarse-grained classification is performed as a preliminary action at edge nodes before data transmission. This pre-processing step filters out unnecessary data, so that only relevant information needs to be transmitted to cloud nodes for fine-grained classification, reducing overall bandwidth consumption while preserving accuracy.
4Adaptability or versatility
If new object categories are added, then system adaptability is improved, but retraining requirements increase
Solution Approach 1:
The classification model is segmented into hierarchical levels: coarse-grained categories at edge nodes and fine-grained categories at cloud nodes. When new categories are added, the system can update the hierarchical structure without requiring complete retraining of all nodes, reducing retraining time while maintaining adaptability.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for monitoring. The method includes receiving, at a second node, a target image from a first node, where the target image is determined as being of a first category based on performing target detection on a road monitoring image obtained by the first node, and the second node is closer to a cloud end than the first node. The method further includes determining, at the second node, a second category of the target image, where the second category is a subcategory of the first category; and in response to the second category being a preset category, sending a warning corresponding to the second category to a terminal device associated with the road monitoring image. According to the method of the embodiments of the present disclosure, the accuracy of road monitoring warnings can be improved.