The layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved DeepLabv3++ model. The method reduces the number of model parameters and training time by replacing the backbone network. To improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet, and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model.

Layout analysis of elements is indispensable in graphic design, and layout design can make the audience receive visual information as well as more attractive. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved deeplabv3+ model. The method reduces the number of model parameters and training time by replacing the backbone network, and in order to improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model.

Automated analysis of packaging labels poses significant challenges in computational efficiency and adaptability to complex layouts. This study proposes a lightweight segmentation framework integrating depthwise separable convolution and multi-scale feature enhancement. The conventional Xception backbone is replaced with MobileNetv3-small, reducing model parameters by 68% (from 13.1M to 4.2M). An enhanced Atrous Spatial Pyramid Pooling (ASPP+) module with multi-level dilation rates (2, 4, 8, 12, 16) effectively addresses multi-scale feature extraction for sub-millimeter elements (e.g., 0.2mm pharmaceutical batch codes), high-density text, and intricate graphics. A specialized dataset of 1,000 high-resolution packaging labels (average 3500×5200 pixels) across food, pharmaceutical, and cosmetic categories is constructed, featuring pixel-level semantic annotations and eight industrial layout classes, partitioned into training, validation, and test sets at a 7:2:1 ratio. The proposed model achieves 81.3% mIoU on the test set, outperforming FCN (71.5%), PSPNet (75.0%), and the original DeepLabv3+ (77.6%) by 9.8, 6.3, and 3.7 percentage points, respectively. Inference speed reaches 0.32 seconds per frame (NVIDIA RTX 4080 Ti). For critical industrial metrics, the framework attains 97.5% accuracy in overlapping element segmentation, constrains edge errors for 0.2mm batch codes to < 1 pixel (ISO 15378 compliant), and reduces false detection rates for semi-transparent labels by 18.3% (F-score: 0.891). Integrated with a GoogLeNet-based layout classifier (90.56% accuracy), this system provides an efficient, high-precision solution for automated compliance verification and intelligent packaging design.