In this article, we introduce two novel methods to detect adversarial examples utilizing pixel value diversity. First, we propose the concept of pixel value diversity (which reflects the spread of pixel values in an image) and two independent metrics (UPVR and RPVR) to assess the pixel value diversity separately. Then we propose two methods to detect adversarial examples based on the threshold method and Bayesian method respectively. Experimental results show that compared to an excellent prior method LID, our proposed methods achieve better performances in detecting adversarial examples. We also show the robustness of our proposed work against an adaptive attack method.