Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model

Visual impairment, such as blindness, can have a profound impact on an individual's cognitive and psychological functioning. Therefore, the use of assistive techniques can help alleviate the adverse effects and enhance the quality of life for people who are blind. Most existing research primarily focuses on mobility, navigation, and object detection, with aesthetics receiving comparatively less attention, despite notable advancements in smart devices and innovative technologies for visually impaired individuals. Object detection is a crucial aspect of computer vision (CV), which involves classifying objects within images, enabling applications such as image retrieval, augmented reality, and many more. In recent times, deep learning (DL) techniques have become a powerful approach for extracting feature representations from data, leading to significant advancements in the field of object detection. In this paper, an enhanced assistive Technology for Blind People through Object Detection Using a Hiking optimization algorithm (EATBP-ODHOA) technique is proposed. The primary objective of the EATBP-ODHOA technique is to develop an effective object detection model for visually impaired individuals by utilizing advanced DL techniques. The image pre-processing stage initially employs an adaptive bilateral filtering (ABF) technique to improve image quality by removing unwanted noise. Furthermore, the Faster R-CNN model is used for the object detection process. Moreover, the EATBP-ODHOA method utilizes fusion models such as ResNet and DenseNet-201 for the feature extraction process. Additionally, the bidirectional gated recurrent unit (Bi-GRU) method is employed for the classification process. Finally, the parameter tuning of the fusion models is performed by using the Hiking Optimisation Algorithm (HOA) method. The experimentation of the EATBP-ODHOA model is performed under the indoor object detection dataset. The comparison analysis of the EATBP-ODHOA model revealed a superior accuracy value of 99.25% compared to existing approaches.