Quality, quantity and generality in the evaluation of object detection algorithms

Christian Wolf 1, 2 Jean-Michel Jolion 2
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures. In this paper we propose an approach to evaluation which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the Image Eval text detection competition.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01527426
Contributor : Équipe Gestionnaire Des Publications Si Liris <>
Submitted on : Wednesday, May 24, 2017 - 1:46:40 PM
Last modification on : Tuesday, February 26, 2019 - 1:46:00 PM

Identifiers

  • HAL Id : hal-01527426, version 1

Citation

Christian Wolf, Jean-Michel Jolion. Quality, quantity and generality in the evaluation of object detection algorithms. Image Eval 2007, Jun 2007, Amsterdam, NL, Netherlands. ⟨hal-01527426⟩

Share

Metrics

Record views

122