Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions
This provides a quantitative explanation for model decisions, aiding in verification and trust for users in domains like autonomous driving, though it is incremental as it builds on existing explainability methods.
The paper tackled the problem of understanding object detection models' reliance on scene-level context by proposing a black-box explainability method that measures the impact of background objects on detection accuracy, finding that buildings and people significantly affect the detection of emergency vehicles with a YOLOv8 model.
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.