Study on real-time warning system of blind path for the visually impaired based on improved deep residual shrinkage network

Visually impaired individuals often face various obstacles when navigating blind roads, such as road disconnections, obstructions, and more complex road emergencies, which can leave them in difficult situations. Traditional early warning methods suffer from low accuracy and lack real-time warning capabilities. Therefore, this study proposes a novel real-time warning system for traffic jams on blind roads. By analyzing the emotional state (normal, mild anxiety, extreme anxiety) from the electroencephalogram (EEG) signals of visually impaired individuals when they are trapped, the system can determine whether they are in distress and require assistance. Additionally, considering the complexity of the road environment and the fact that EEG signals are prone to external interference during acquisition, this study introduces an improved deep residual shrinkage network based on dense blocks (DB-DRSN). DB-DRSN replaces the convolutional hidden layer in the original residual shrinkage module with dense blocks and integrates dense connections to optimize the use of both shallow and deep features. The results show that the system achieves an accuracy of 96.72% in recognizing the difficulties faced by the visually impaired, significantly outperforming traditional models. Compared to other warning methods, the proposed system offers quicker assistance to visually impaired individuals. The real-time warning system based on DB-DRSN demonstrated strong performance in detecting and warning about blind road jams, greatly enhancing the safety of visually impaired individuals and enabling timely detection and intervention.