In recent years, drones have played increasingly important roles in various low-altitude production and service scenarios. Due to the limited storage and computing resources of edge computing platforms, the deployment performance of vision models becomes a bottleneck for various algorithm applications. The long-tail distribution of activation values in convolutional neural network (CNN)-Transformer hybrid models causes substantial quantization accuracy deterioration, an efficient post-training quantization method is proposed, which this study addresses by suggesting an effective post-training quantization technique. This method uses activation noise compensation (ANC) and adaptive difficulty migration to suppress the impact of outliers on quantization accuracy and improve inference efficiency. According to experimental results, the proposed method achieves less than 1% accuracy loss on typical models under 8-bit quantization, and it improves inference time after quantization by at most 200%. In summary, the proposed method significantly enhances vision model inference performance on edge devices and supports model deployment on low-altitude drone platforms.