Edge detection is especially important for computer vision and generally for image processing and visual recognition. On the other hand, digital image processing is widely used in multiple science fields such as medicine, X-ray analysis, magnetic resonance tomography, computed tomography, and cosmology, i.e. information collection from satellites, its transferring, and analysis. Any step of image processing, from obtaining the image to its segmentation and object recognition is followed by image noise. The processing speed is vital in popular fields that demand image analysis in real time. In this work, we have proposed an approach of parallel computing of the Canny algorithm using CUDA technology, the complexity of object recognition is analyzed according to the type of the image noise and the level of its density. The sequenced implementation on GPU and the parallel implementation on GPU was considered. The results were analyzed for efficiency and reliability. Also, parallel acceleration is calculated according to the size of the incoming image. The manipulations with the image showed the growth of processing speed of 68 times, whereas the manipulations with the size of the kernel showed the growth of processing speed of 26 times. Another contribution of this work is the analysis of the complexity of object recognition depending on the type of image noise and the level of its density. Furthermore, the increase of Gaussian noise density linearly increases the complexity of object recognition.