Aiming at the problem of treating uncertainty modeling as a step for optimizing prediction results while ignoring the value of uncertainty itself in current object detection algorithms， an object detection result evaluation scheme based on Guide to the Expression of Uncertainty in Measurement （GUM） was proposed. Firstly， sources of uncertainty in object detection were decomposed into three mutually independent aspects： data， model and platform. Then， uncertainty influence factors were extracted from these three aspects to construct measurement function. Secondly， type A and type B evaluation methods in GUM were used to measure uncertainty influence components. Finally， uncertainty synthesis rules were used on the basis of the measurement function， and standard uncertainty was synthesized from uncertainty components. Experiments were conducted by using an object detection algorithm. The results show that compared to Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM）， the data uncertainty increased 5.30 and 19.08 percentage points respectively in capturing noisy data； model uncertainty has a tiny influence on the prediction results， which can be ignored within the range of 10^-6； platform uncertainty can represent prediction result differences caused by software and hardware platforms in numerical form.