Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization.

Visually impaired individuals face daily challenges in social engagement and routine activities due to limited access to real-time environmental information. Damage detection is a common approach in infrastructure that combines steel and concrete reinforcement to achieve optimal durability and structural strength. These bridges, designed to withstand diverse loads such as seismic forces, traffic weight, and environmental factors, are significant for maintaining structural integrity. Damage detection comprises applying advanced structural health monitoring methods to identify and assess potential deterioration or damage in concrete bridge components. Machine learning (ML) models, pattern detection, and statistical analysis are extensively adopted to identify subtle changes and process sensor information in structural response that might indicate corrosion, cracks, or other structural problems. Earlier detection and continuous monitoring of damage enable prompt intervention, ensuring longevity and safety while reducing the need for extensive repairs or the risk of unexpected failures. This study proposes an Automated Damage Detection using a Modified Seagull Optimizer with Ensemble Learning (ADD-MSGOEL) method for visually impaired people. The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. Initially, the ADD-MSGOEL method utilizes contrast enhancement (CLAHE) to enhance the image quality. Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. At last, an ensemble of three models, namely long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and sparse autoencoder (SAE) models, are implemented for the classification and detection of the damages. To demonstrate the effectiveness of the ADD-MSGOEL technique, a series of experiments were conducted using the CODEBRIM dataset. The experimental validation of the ADD-MSGOEL technique portrayed a superior accuracy value of 97.59% over existing models.