ODEI: Object Detector Efficiency Index
Object detectors often rely on multiple metrics to reflect their accuracy and speed performances independently. This article introduces object detector efficiency index (ODEI), a hardware-agnostic metric designed to assess object detector efficiency based on speed-normalized accuracy, utilizing established concepts including mean average precision (mAP) and floating-point operations (FLOPs). By defining seven mandatory parameters that must be specified when ODEI is invoked, the article aims to clarify long-standing confusions within literature regarding evaluation metrics and promote fair and transparent benchmarking research in the object detection space. Usage demonstration of ODEI using state-of-the-art (SOTA) YOLOv12 and RT-DETRv3 studies is also included.
Authors
- Wenan Yuan (ORCID: https://orcid.org/0000-0002-6300-2973)
Publication Details
- Journal
- AI
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3390/ai6070141
- Citations
- 1
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.28