On the black-box explainability of object detection models for safe and trustworthy industrial applications

In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstanding results and are widely used in critical domains like autonomous driving and video surveillance. However, their adoption in high-risk applications, where errors may cause severe consequences, remains limited. Explainable Artificial Intelligence methods to address this issue, but many existing techniques are model-specific and designed for classification making them less effective for object detection and difficult for non-specialists to interpret. In this work focus on model-agnostic explainability methods for object detection models and propose D-MFPP, an extension of the Morphological Fragmental Perturbation Pyramid (MFPP) technique based on segmentation-based to generate explanations. Additionally, we introduce D-Deletion, a novel metric combining faithfulness localization, adapted specifically to meet the unique demands of object detectors. We evaluate these methods on real-world industrial and robotic datasets, examining the influence of parameters such as the number masks, model size, and image resolution on the quality of explanations. Our experiments use single-stage detection models applied to two safety-critical robotic environments: i) a shared human-robot workspace safety is of paramount importance, and ii) an assembly area of battery kits, where safety is critical due potential for damage among high-risk components. Our findings evince that D-Deletion effectively gauges performance of explanations when multiple elements of the same class appear in a scene, while D-MFPP provides a promising alternative to D-RISE when fewer masks are used.