Image processing algorithms include methods that process images from their acquisition to the extraction of useful information for a given application. Among interpretation algorithms, some are designed to detect, localize, and identify one or several objects in an image. The problem addressed is the evaluation of the interpretation results of an image or a video given an associated ground truth. Challenges are multiple, such as the comparison of algorithms, evaluation of an algorithm during its development, or the definition of its optimal settings. We propose a new metric for evaluating the interpretation result of an image. The advantage of the proposed metric is to evaluate a result by taking into account the quality of the localization, recognition, and detection of objects of interest in the image. Several parameters allow us to change the behavior of this metric for a given application. Its behavior has been tested on a large database and showed interesting results.