Rafael Padilla

TL;DR: This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms and proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.

TL;DR: This work provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages, and 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.

TL;DR: This work focuses of the evaluation of face detection classifiers minding facial landmarks, widely used by researchers in order to detect the location of faces and objects in a given image.

TL;DR: The test results indicate that the two proposed approaches can lead to very significant improvements in the efficiency of the otherwise manual annotation processes, which are typically error prone and very time consuming.

TL;DR: The results obtained during the simulations showed that the team (consisting of two players) using the evaluation function with its coefficients optimized by the GA won in more than 69.18% of the total matches.