Improving the validation of multiple-object detection using a

Although many of today’s object detectors (ODs) are fairly powerful and advanced, most of them still suffer from high detection failure rates. To address this issue, we have developed an innovative, multiple-object detection validation method using a complex-network-community-based relevance metric. This metric aims to measure the relevance of multiple objects in the same OD output, based on our observation that a faulty OD output generally includes objects that are irrelevant or unrelated to each other. To verify the effectiveness of our method, we formulated four research questions, and performed an experiment with statistical analyses to address these questions. Our experiment provides strong support that our method (particularly the relevance metric) is highly effective at helping human testers in identifying faulty OD outputs. • Many of today’s object detectors (ODs) suffer from high detection failure rates. • Many ODs ignore the relevance among multiple object instances in the same OD output. • We develop a relevance metric for each object pair in an OD output and use it for validation. • Our method is effective at helping testers in identifying potentially faulty OD outputs.