The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models.

翻译：人工智能在边缘摄像头系统人体检测中的广泛应用催生了高精度但复杂的模型，这些模型难以解释和调试。本研究提出了一种利用可解释人工智能进行模型调试的诊断方法，通过专家驱动的问题识别与解决方案构建。我们在真实办公边缘网络中的Bytetrack模型上验证发现，训练数据集是主要偏差来源，并建议采用模型增强作为解决方案。本方法有助于识别模型偏差，这对于构建公平且值得信赖的模型至关重要。