Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions

TL;DR

This paper introduces a black box explainability method that quantifies how scene level objects influence object detection decisions, revealing model dependencies beyond standard metrics.

Contribution

It proposes a novel approach to measure the impact of scene objects on detection accuracy, enhancing understanding of model decision processes.

Findings

Scene objects significantly affect detection accuracy.

Model reliance on scene context can be quantitatively assessed.

Method reveals hidden dependencies in object detection models.

Abstract

Although accuracy and other common metrics can provide a useful window into the performance of an object detection model, they lack a deeper view of the model's decision process. Regardless of the quality of the training data and process, the features that an object detection model learns cannot be guaranteed. A model may learn a relationship between certain background context, i.e., scene level objects, and the presence of the labeled classes. Furthermore, standard performance verification and metrics would not identify this phenomenon. This paper presents a new black box explainability method for additional verification of object detection models by finding the impact of scene level objects on the identification of the objects within the image. By comparing the accuracies of a model on test data with and without certain scene level objects, the contributions of these objects to the model's performance becomes clearer. The experiment presented here will assess the impact of buildings and people in image context on the detection of emergency road vehicles by a fine-tuned YOLOv8 model. A large increase in accuracy in the presence of a scene level object will indicate the model's reliance on that object to make its detections. The results of this research lead to providing a quantitative explanation of the object detection model's decision process, enabling a deeper understanding of the model's performance.