The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical applications, it is vital to prevent errors. To address this challenge, we propose a self-monitoring framework that allows for the perception system to perform plausibility checks at runtime. We show that by incorporating an additional component for detecting human body parts, we are able to significantly reduce the number of missed human detections by factors of up to 9 when compared to a baseline setup, which was trained only on holistic person objects. Additionally, we found that training a model jointly on humans and their body parts leads to a substantial reduction in false positive detections by up to 50% compared to training on humans alone. We performed comprehensive experiments on the publicly available datasets DensePose and Pascal VOC in order to demonstrate the effectiveness of our framework. Code is available at https://github.com/ FraunhoferIKS/smf-object-detection.

翻译：在现实应用中，自主系统必须具备检测学习对象（无论其外观如何）的能力。尤其在安全关键型应用中，检测人物通常是基础任务，因此防止错误至关重要。为解决这一挑战，我们提出了一种自监控框架，使感知系统能够在运行时执行合理性检查。我们证明，通过引入一个检测人体部件的额外组件，与仅训练了整体人物对象的基准设置相比，我们将漏检人数最多减少了9倍。此外，我们发现，联合训练人物及其身体部件的模型，与仅训练人物相比，误检率最多降低了50%。我们在公开数据集DensePose和Pascal VOC上进行了全面实验，以证明我们框架的有效性。代码可在https://github.com/FraunhoferIKS/smf-object-detection获取。