Model-agnostic Body Part Relevance Assessment for Pedestrian Detection Model Benchmarking

Model-agnostic explanation methods for deep learning models are flexible regarding usability and availability. However, due to the fact that they can only manipulate input to see changes in output, they suffer from weak performance when used with complex model architectures. For models with large inputs as, for instance, in object detection, sampling-based methods like KernelSHAP are inefficient due to many computation-heavy forward passes through the model. In this work, we present a framework for using sampling-based explanation methods in a computer vision context shown for body part relevance assessment for pedestrian detection. Furthermore, we introduce a novel sampling-based method similar to KernelSHAP that shows more robustness for lower sampling sizes and, thus, is more efficient for explainability analyses on large-scale datasets. We demonstrate our relevance assessment method on simulation data acquired with the CARLA simulator. In the end, our method enables the benchmarking and performance comparison of various pedestrian detection models based on human-interpretable semantic regions.