Ashish Kumar Singh supervised by Dr. Azeemuddin Syed received his Master of Science in Electronics and Communication Engineering (ECE). Here’s a summary of his research work on Facade Foresight: Transformative Approaches in Semantic Segmentation for Building Inspection:
This research delves into the application of advanced semantic segmentation techniques to automate facade inspection, a critical component in the maintenance and safety assessment of urban infrastructures. Central to the study is the WALL-E dataset, a comprehensive collection specifically curated to encompass a wide range of facade defects, including cracks, stains, and delamination. This dataset is designed to challenge and refine the capabilities of deep learning models by exposing them to a diverse array of defect types and complex architectural features, thereby mimicking the variety of conditions encountered in real-world settings. The training methodology employed in this study leverages the strengths of the DeepLabV3+ model, compared against other leading models such as UNet, PSPNet, and FCN. Each model was rigorously trained and evaluated on their ability to accurately segment defects across different types of building materials and architectural styles. DeepLabV3+ emerged as the standout model due to its sophisticated architecture that combines atrous convolution with an encoder-decoder structure, enhancing its ability to capture detailed spatial information and accurately delineate defect boundaries. A significant portion of the evaluation focused on the generalization capabilities of the models. DeepLabV3+ demonstrated remarkable adaptability, not only performing well on the diverse images of the WALL-E dataset but also showing robust generalization on arbitrary internet images and out-of-distribution samples. This indicates the model’s ability to extend beyond its training parameters and adapt to new, unseen scenarios, which is essential for practical applications where variable conditions are common. Further assessments were made on images without defects to evaluate the model’s precision in identifying defect-free areas, thereby reducing the likelihood of false positives. This aspect of the model’s performance is critical in practical scenarios, where the ability to distinguish between damaged and intact structures can significantly impact maintenance decisions and safety assessments. This thesis highlights the significant potential of using semantic segmentation in the realm of structural health monitoring. The advanced capabilities of DeepLabV3+, coupled with the comprehensive and diverse WALL-E dataset, set a new benchmark in the field, providing a strong foundation for future research and practical applications in facade inspection. This work not only advances the technology in automated building inspections but also opens up new avenues for further enhancements and applications in urban infrastructure maintenance.
October 2024