<p>Eyes are highly efficient sensors created by nature, capable of perceiving external light signals with remarkable precision. The signals received by the eyes undergo intricate processing in the visual cortex, resulting in the phenomenon of vision. Among the various aspects of visual processing, the detection of motion details holds paramount significance for the survival and navigation of organisms. Extensive research has been conducted over the years to comprehend the complex mechanisms underlying motion direction detection in the visual system. In our previous work, we developed the HRC-based artificial visual system for motion direction detection. However, the model was designed solely for processing images with a single input channel and may not directly apply to colored images. In this paper, we present a novel approach to motion direction detection that supports colored images by integrating photoreceptors with different spectral sensitivities. The experiment demonstrates that incorporating color information enhances motion vision capabilities, aligning with biological theories. In this research, we expanded our model to include colored images. The process of constructing the motion direction detecting model for grayscale and colored images follows a similar trajectory. Our initial step involved constructing the core detector, which employs the HRC model to detect motion in a single direction. According to the HRC model, direction-selective neurons receive signals from two separate photoreceptors to detect motion direction. Subsequently, we developed a contrast-response system that receives input from the same photoreceptors and inhibits motion-direction-detecting neurons based on the contrast information of the input signals. Furthermore, we extended the model to two-dimensional planes to detect eight movement directions. In the two-dimensional model, the contrast-response system receives input from a number of surrounding photoreceptors and outputs an inhibitory signal to the motion-direction-detecting neurons based on the contrast information from the photoreceptors. Finally, we constructed a global motion-direction-detecting model. To demonstrate the practicality of the model, a comprehensive comparison was conducted with four deep learning models, including two types of convolutional neural networks, EfficientNetB0 and ResNet-50. The results of the comparison reveal that the proposed model outperforms the deep learning models in terms of accuracy and noise immunity.</p>