Performance evaluation of semantic segmentation models: a cross meta-frontier DEA approach

Min Yang, Zixuan Wang, Liang Liang and Qingxian An

Journal of the Operational Research Society, 2024, vol. 75, issue 12, 2283-2297

Abstract: Performance evaluation of semantic segmentation models is an essential task because it helps to identify the best-performing model. Traditional methods, however, are generally concerned with the improvement of a single quality or quantity. Moreover, what causes low performance usually goes unnoticed. To address these issues, a new cross meta-frontier data envelopment analysis (DEA) approach is proposed in this article. For evaluating model performance comprehensively, not only accuracy metrics, but also hardware burden and model structure factors, are taken as DEA outputs and inputs, separately. In addition, the potential inefficiency is attributed to architectures and backbones via efficiency decomposition, so that it can find the sources of inefficiency and provides a direction for performance improvement. Finally, based on the proposed approach, the performance of 16 classical semantic segmentation models on the PASCAL VOC dataset are re-evaluated and explained. The results verify that the proposed approach can be considered as a comprehensive and interpretable performance evaluation technique, which expands the traditional accuracy-based measurement.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2313116 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:75:y:2024:i:12:p:2283-2297

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2313116

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().