Existing $N$-point interpolation methods generate large errors in the average precision calculation for object detection. These errors lead to average precision distortion, which makes it impossible to accurately evaluate the performance of the model. We investigate the reason for the average precision distortion and propose an enhanced $N$-point interpolation method. These improvements are based on the $N$-point interpolation method and can be summarized in two parts: (1) The interpolation point position is changed to the middle interpolation. (2) Dynamic selection of parameters for calculating the area of the interpolation interval. Experiments verify the existence of severe average precision distortion in the $N$-point interpolation method. Furthermore, the proposed enhanced $N$-point interpolation method reduces the average precision distortion by more than 90% to only 0.04%. In this way, the enhanced $N$-point interpolation method is able to replace the all-point interpolation method for fast and accurate evaluation of object detection model.

现存的$ñ$点插值方法在目标检测的平均精度计算中会产生很大的误差。这些误差导致平均精度失真，从而无法准确评估模型的性能。我们调查了平均精度失真的原因，并提出了一个增强的$ñ$点插值法。这些改进基于$ñ$点插补方法，可概括为两部分： (1) 将插补点位置改为中间插补。(2)动态选择计算插值区间面积的参数。实验验证了存在严重的平均精度失真$ñ$点插值法。此外，建议的增强$ñ$点插值法将平均精度失真降低了 90% 以上，仅为 0.04%。这样，增强的$ñ$点插值法能够替代全点插值法，对目标检测模型进行快速准确的评价。