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

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
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article

ODEI: Object Detector Efficiency Index

Wenan Yuan
1 citations
AI
Advanced Neural Network Applications
1.28
article

ODEI: Object Detector Efficiency Index

Wenan Yuan
article en
1 citations

Abstract

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.

AIVol. 6(7)
Openalex Percentile: Top 19%
Advanced Neural Network Applications
1.28
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