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.

翻译：尽管准确率和其他常见指标能够为评估目标检测模型性能提供有用视角，但它们缺乏对模型决策过程的深层洞察。无论训练数据和流程的质量如何，目标检测模型所学习到的特征都无法得到保证。模型可能会学习到特定背景上下文（即场景级目标）与标注类别存在性之间的关联。此外，标准性能验证和指标也无法识别这一现象。本文提出了一种新的黑盒可解释性方法，通过发现场景级目标对图像内目标识别的影响，对目标检测模型进行额外验证。通过比较模型在包含与排除特定场景级目标的测试数据上的准确率，这些目标对模型性能的贡献将变得更加清晰。本文实验将评估图像上下文中的建筑物和行人对象对经微调YOLOv8模型检测应急道路车辆的影响。若场景级目标存在时准确率大幅提升，则表明模型依赖该目标进行检测。本研究结果为定量解释目标检测模型的决策过程提供了依据，从而加深对模型性能的理解。