A study of Isual perceptual target monitoring in graphic design based on Multi-Task structured learning and interaction mapping

In graphic design, many materials come from images and videos, but the current visual target analysis still suffers from the disadvantages of poor results and not being able to understand the semantic information required by graphic design. In order to solve the above problems, this study builds a visual perception target monitoring network model by combining multi-task structured learning and interaction mapping detection methods, and based on the combined detection method. The study first analyses the target detection effect of the combined detection method, and the results show that compared with other methods, the ROC curve area of the method used in this paper is larger and the accuracy is higher, up to 96.45 %, and the maximum accuracy of the detection method is 90.00 %. Then the target tracking effect of the combined detection method is analysed, and the average success rate of the proposed method in multi-target tracking is maximum 99.69 %. Finally, the model’s effectiveness in target classification and identification is analysed, and the results show that the classification error rate of the network model based on the detection method is 4.99 %, which is lower than other models. From the above results, it can be seen that the visual perception target monitoring network model based on multi-task structured learning and interaction mapping detection method proposed in the study can achieve visual target perception and has certain application value in graphic design.