Enterprise AI Analysis
Artistic Collaborative Design Optimization for Urban Public Architecture
By Miao Wang & Kai Zhang - Published: 06 April 2026
Transforming Architectural Design with AI
This study introduces a novel human-machine collaborative design paradigm, leveraging an improved Faster R-CNN with artificial intelligence to address challenges in architectural art design. Our framework moves beyond subjective experience to data-driven decision-making, significantly enhancing design efficiency and quality.
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Overall mAP@0.5
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mAP Improvement vs. Baseline
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Avg. Design Cycle Reduction
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Normative Compliance Adoption Rate
Deep Analysis & Enterprise Applications
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Enhanced Detection Accuracy for Artistic Elements
Our improved model achieved an overall mAP@0.5 of 73.5% and recall of 75.6% on the PB-Art12 dataset, a statistically significant improvement of 8.4 percentage points in mAP compared to the baseline Faster R-CNN. This performance advantage is particularly remarkable for small-scale, complex-texture artistic elements such as reliefs and mural paintings, with mAP improved by over 10%. This validates the effectiveness of the introduced multi-scale feature fusion and texture enhancement modules. The model's improvement on small-scale targets is +12.1 percentage points and for high complexity textures is +12.3 percentage points.
Quantitative Style Recognition Capability
The proposed style-aware attention module significantly enhances the model's ability to capture high-level style semantics. In a dedicated style classification experiment, features extracted from our improved model achieved an average classification accuracy of 85.2%, substantially outperforming the baseline Faster R-CNN's 72.8%. This demonstrates the module's success in encoding semantic information related to complex architectural art styles, enabling objective quantitative analysis of subjective artistic attributes.
Streamlined Collaborative Design Workflows
Empirical analysis shows that the AI-assisted collaborative system effectively shortens the design cycle, reducing the average iteration rounds from 5.2 to 2.8. The system's suggestions have a high overall adoption rate of 68.4%, with normative suggestions (e.g., dimension compliance) reaching 92.1% adoption. Aesthetic optimization suggestions are adopted 61.5% of the time, serving as important reflection points for designers. This accelerates the design process and ensures higher compliance and style consistency.
Contribution of Key Architectural AI Components
A rigorous ablation study confirms the individual contributions of our improvement measures. Architectural image domain pre-training provided the largest single-step gain (+2.7 mAP, +2.5 recall), highlighting the importance of transferring general visual knowledge. The improved multi-scale strategy further enhanced detection, especially for multi-scale targets. Both the texture enhancement module and the style-aware attention module were crucial for improving accuracy in complex texture categories and achieving a high style consistency score of 0.85 (Model A5).
Enterprise Process Flow
Problem Definition
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Data Foundation & Model Improvement
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System Construction
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Model Performance Validation
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Empirical Study on Collaborative Efficiency
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Comprehensive Discussion of Results
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