A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
| Autor: | Eduardo A. B. da Silva, Wesley L. Passos, Thadeu L. B. Dias, Rafael Padilla, Sergio L. Netto |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
evaluation Computer Networks and Communications Computer science lcsh:Electronics automatic assessment Representation (systemics) recall lcsh:TK7800-8360 020206 networking & telecommunications 02 engineering and technology Object detection bounding boxes Open source Hardware and Architecture Control and Systems Engineering Signal Processing Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing precision Electrical and Electronic Engineering object-detection metrics |
| Zdroj: | Electronics Volume 10 Issue 3 Electronics, Vol 10, Iss 279, p 279 (2021) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10030279 |
| Popis: | Recent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated datasets. Annotation tools represent the location and size of objects in distinct formats, leading to a lack of consensus on the representation. Such a scenario often complicates the comparison of object detection methods. This work alleviates this problem along the following lines: (i) It provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages (ii) it examines the most used annotation formats, showing how different implementations may influence the assessment results and (iii) it provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets. In addition, this work proposes a new metric, also included in the toolkit, for evaluating object detection in videos that is based on the spatio-temporal overlap between the ground-truth and detected bounding boxes. |
| Databáze: | OpenAIRE |
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