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.

Summary: Fault detection and diagnosis are fundamental problems in the process of abnormal event detection in oil wells. This paper describes an open-source modular system that enables the efficient design of fault detectors and classifiers based on machine learning techniques. Events considered in this work are part of the publicly available 3W database developed by Petrobras, the Brazilian oil holding. Seven fault classes are considered, with distinct dynamics and patterns, as well as several instances of normal operation. We also show the effectiveness of the use of wavelet-based features, which provide multiscale time-frequency analysis, targeting a more realistic event modeling. A few challenges imposed by the 3W data set are addressed by combining both wavelet and statistical features, resulting in more accurate and more robust classifiers, with a 98.6% balanced accuracy in the multiclass problem, a significant improvement over the 94.2% previously reported in the literature.