目标检测模型的决策依据与可信度分析

目标检测模型已经在很多领域得到广泛应用, 但是, 作为一种机器学习模型, 对人类来说仍然是一个黑盒. 对模型进行解释有助于我们更好地理解模型, 并判断其可信度. 针对目标检测模型的可解释性问题, 提出将其输出改造为关注每一类物体存在性概率的具体回归问题, 进而提出分析目标检测模型决策依据与可信度的方法. 由于原有图像分割方法的泛用性较差, 解释目标检测模型时, LIME所生成解释的忠诚度较低、有效特征数量较少. 提出使用DeepLab代替LIME的图像分割方法, 以对其进行改进. 改进后的方法可以适用于解释目标检测模型. 实验的对比结果证明了所提出改进方法在解释目标检测模型时的优越性.

Decision Basis and Reliability Analysis of Object Detection Model

The object detection model has been widely applied in many fields; however, as a machine learning model, it remains a black box to humans. Interpreting the model is conducive to a better understanding of the model and can help judge whether the model is reliable. In view of the interpretability problem of the object detection model, this study proposes that the output of the model should be changed into a specific regression problem that focuses on the existence possibility of the objects of each class. On this basis, the methods to analyze the decision basis and reliability of the object detection model are put forward. Due to the poor versatility of the original image segmentation method, LIME generates unfaithful and ineffective interpretations when interpreting the object detection model. Therefore, the image segmentation method with LIME replaced by DeepLab is put forward and improved, and the improved method can interpret the object detection model. The experiment results prove the superiority of the improved method in interpreting the object detection model.