Prioritizing Testing Instances to Enhance the Robustness of Object Detection Systems

Shihao Weng, Yang Feng, Yining Yin, Jia Liu

Abstract: Object detection models have been widely deployed in military and life-related intelligent software systems. However, along with the outstanding success of object detection, it may exhibit abnormal behavior and lead to severe accidents and losses. During the development and evaluation process, training and evaluating an object detection model are computationally intensive, while preparing annotated tests requires extremely heavy manual labor. Therefore, reducing the annotation budget of test data collection becomes a challenging and necessary task. Although many test prioritization approaches for DNN-based systems have been proposed, the large differences between classification and object detection make them difficult to apply to testing object detection models.In this paper, we propose DeepView, a novel instance-level test prioritization tool for object detection models to reduce data annotation costs. DeepView first splits the object detection results into instances, and then computes the localization and classification capabilities of the instances, respectively. Next, we design a test prioritization tool that enables testers to improve model performance by focusing on instances that may cause model errors from a large unlabeled dataset. To evaluate DeepView, we conduct extensive experiments on two kinds of object detection model architectures and two commonly used datasets. The experimental results show that DeepView outperforms existing test prioritization approaches regarding effectiveness and diversity. Also, we observe that using DeepView can effectively improve the accuracy and robustness of object detection models.