For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

中文翻译：

---

基于摄像头的自动车辆目标检测性能分析。

为了安全地向市场推出自动驾驶汽车，必须有效地识别和评估整个系统以及子组件的风险。这也包括使用人工智能算法的基于相机的物体检测。琐碎的和可解释的是，由于相机的原理，性能在很大程度上取决于环境条件，并且可能很差，例如在大雾中。但是，还有其他因素会影响基于相机的目标检测的性能，本文将对此进行首次全面研究。此外，由于使用了基于人工智能的算法，因此无法对检测性能进行精确建模以及对单个检测结果进行解释。因此，提出了一种基于所调查影响因素的建模方法，并采用新近开发的SHapley Additive ExPlanations（SHAP）方法来分析和说明不同物体检测算法的检测性能。结果表明，无论使用哪种检测算法，许多影响因素（例如，对象相对于摄像机的相对旋转或对象在图像上的位置）对检测性能的影响基本相同。尤其是，被测物体检测器所揭示的弱点可用于推导具有挑战性和关键性的场景，以进行自动车辆的测试和型式认可。