Packaging style design based on visual semantic segmentation technology and intelligent cyber physical system.

The integration of image segmentation technology into packaging style design significantly amplifies both the aesthetic allure and practical utility of product packaging design. However, the conventional image segmentation algorithm necessitates a substantial amount of time for image analysis, rendering it susceptible to the loss of vital image features and yielding unsatisfactory segmentation results. Therefore, this study introduces a novel segmentation network, G-Lite-DeepLabV3+, which is seamlessly incorporated into cyber-physical systems (CPS) to enhance the accuracy and efficiency of product packaging image segmentation. In this research, the feature extraction network of DeepLabV3 is replaced with Mobilenetv2, integrating group convolution and attention mechanisms to proficiently process intricate semantic features and improve the network's responsiveness to valuable characteristics. These adaptations are then deployed within CPS, allowing the G-Lite-DeepLabV3+ network to be seamlessly integrated into the image processing module within CPS. This integration facilitates remote and real-time segmentation of product packaging images in a virtual environment.Experimental findings demonstrate that the G-Lite-DeepLabV3+ network excels at segmenting diverse graphical elements within product packaging images. Compared to the original DeepLabV3+ network, the intersection over union (IoU) metric shows a remarkable increase of 3.1%, while the mean pixel accuracy (mPA) exhibits an impressive improvement of 6.2%. Additionally, the frames per second (FPS) metric experiences a significant boost of 22.1%. When deployed within CPS, the network successfully accomplishes product packaging image segmentation tasks with enhanced efficiency, while maintaining high levels of segmentation accuracy.