Intelligent Sensing Evaluation Using Scenario-Weighted Test Samples
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Solution Overview
Problem
Existing intelligent sensing systems lack a reliable method to accurately assess their sensing capabilities, making it difficult to determine if they meet specific scenario requirements.
Innovation Solution
A test method and system that involves obtaining test samples with scores and labels, performing scenario presentation, inputting data into the sensing system, comparing results, and calculating a quantitative grade to evaluate sensing capability, using scenario-based samples that can be expanded and categorized for comprehensive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional testing methods are used for intelligent sensing systems, then the testing process is simple, but the measurement precision of sensing capability is insufficient
Solution Approach 1:
The testing system is segmented into multiple independent modules: a data generation module that creates scenario-based test samples, a testing module that executes recognition tasks, and an evaluation module that calculates quantitative grades. This segmentation allows each module to perform its specific function with high precision while maintaining overall system manageability.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that uses scenario-based test samples with assigned scores and labels. This intermediary layer transforms the complex sensing capability assessment into a structured comparison between recognition results and ground truth labels, enabling precise measurement without requiring direct complex measurement of sensing capabilities.
2Adaptability or versatility
If comprehensive scenario-based test samples are used, then the evaluation coverage is improved, but the loss of time for testing increases
Solution Approach 1:
Test samples are prepared in advance with pre-assigned scores, labels, and scenario descriptions. The data generation module creates comprehensive scenario-based samples before the actual testing phase, including various target types, scenarios, and difficulty levels. This preliminary preparation allows the testing phase to proceed efficiently without time-consuming sample creation during execution.
Solution Approach 2:
The evaluation method changes parameters by using weighted scoring systems where different sample types and difficulty levels have different scores. The evaluation module calculates quantitative grades by aggregating results with appropriate weights, allowing comprehensive scenario coverage while managing testing time through parameter-based prioritization and weighting of test cases.
3Reliability
If quantitative grading system is implemented, then the objectivity of assessment is improved, but the device complexity increases
Solution Approach 1:
The evaluation module implements a feedback mechanism that compares recognition results against pre-defined labels and calculates quantitative grades automatically. The system provides objective feedback by computing numerical scores based on matching recognition results with ground truth, eliminating subjective assessment while maintaining system simplicity through automated calculation algorithms.
Solution Approach 2:
The testing system performs self-evaluation by automatically comparing recognition results with pre-defined labels and calculating quantitative grades without external intervention. The evaluation module autonomously processes test results, computes scores based on the grading system, and generates objective assessments, reducing the need for complex external evaluation apparatus.
Data Source
AI summary
An intelligent sensing system can output an identification result about a target on the basis of input data to be identified, the data comprising a scene and the target included in the scene. An intelligent sensing method involves: acquiring a plurality of test samples each including a score value, a label, and first data of the sample; for each sample, determining whether an identification result is correct to obtain a first grade; summing the first grades of all the samples, and dividing the sum by a sum of the score values of all the samples to obtain a second grade for evaluating the sensing capability of the intelligent sensing system. An objective quantitative score can be given to the identification capability of the intelligent sensing system, and the identification capability of the intelligent sensing system can be comprehensively investigated with respect to various scene types.