Aitor Martinez-Seras

Alain Andres, Aitor Martinez-Seras, Ibai Laña et al.

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 aim to address this issue, but many existing techniques are model-specific and designed for classification tasks, making them less effective for object detection and difficult for non-specialists to interpret. In this work we 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 masks to generate explanations. Additionally, we introduce D-Deletion, a novel metric combining faithfulness and 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 of masks, model size, and image resolution on the quality of explanations. Our experiments use single-stage object detection models applied to two safety-critical robotic environments: i) a shared human-robot workspace where safety is of paramount importance, and ii) an assembly area of battery kits, where safety is critical due to the potential for damage among high-risk components. Our findings evince that D-Deletion effectively gauges the 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.

Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad et al.

Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm.