Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms. It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect "true" edges, while suppressing "false" non-edge filter responses. Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex. This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification. Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny's algorithm.