Improving the validation of multiple-object detection using a complex-network-community-based relevance metric
Knowledge-Based Systems(2024)
Abstract
Although many of today’s object detectors (ODs) are fairly powerful and advanced, most of them still suffer from high detection failure rates. To address this issue, we have developed an innovative, multiple-object detection validation method using a complex-network-community-based relevance metric. This metric aims to measure the relevance of multiple objects in the same OD output, based on our observation that a faulty OD output generally includes objects that are irrelevant or unrelated to each other. To verify the effectiveness of our method, we formulated four research questions, and performed an experiment with statistical analyses to address these questions. Our experiment provides strong support that our method (particularly the relevance metric) is highly effective at helping human testers in identifying faulty OD outputs.
MoreTranslated text
Key words
Object detection,Object relevance,Complex network,Community clustering
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined