A Survey on Performance Metrics for Object-Detection Algorithms
Abstract
This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms. Average precision (AP),for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision × recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding-box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding-box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.
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Metrics such as TPR, FPR, and ROC curves that are based on the TN prediction parameter are commonly ignored during evaluation.
“As a consequence, metrics such as True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic (ROC) curves, that are based on the TN prediction parameter are commonly ignored during evaluation [52] .”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [52]
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“95] [27].”
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- Authors
- Padilla, R., Netto, S. L., & da Silva, E. A. B.
- Affiliations
- Universidade Federal do Rio de Janeiro (BR)
- Venue
- 2020 International Conference on Systems, Signals and Image Processing (IWSSIP)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 237-242
- OpenAlex
- W3043995050
- Cited by
- 1,605
- References
- 9
- Citations by year
- 2026: 177 · 2025: 412 · 2024: 407 · 2023: 282 · 2022: 197 · 2021: 105 · 2020: 25
- FWCI
- 46.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Systems, Signals and Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object detection, performance indicators, average precision, precision-recall curve, benchmarking, assessment standardization
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