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An intelligent optimized object detection system for disabled people using advanced deep learning models with optimization algorithm
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OA
AI
DOI：10.1038/s41598-025-00608-z.png)
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Abstract
En 中文
Visually impaired persons face several problems in their day-to-day lives, and technological intermediaries might help them encounter their challenges. Among other beneficial technologies, object detection (OD) is a computer technology related to image processing and computer vision (CV), which identifies and describes objects like vehicles, animals, and persons from digital videos and images. Visually impaired persons (VIPs) can utilize the OD approach for detecting problems and recognizing services to offer secure and informative navigation. Recently, machine learning (ML) and deep learning (DL) have been trained with numerous images of objects, which are highly related to people with disabilities. In this article, a novel Object Detection System for Disabled People Using Advanced Deep Learning Models and Sparrow Search Optimization (ODSDP-ADLMSSO) approach is proposed. The main aim of the ODSDP-ADLMSSO model is to enhance the OD method for visually challenged people. At first, the Gaussian filter (GF) is employed in the image pre-processing stage to remove noise and make the image input data more transparent. In addition, the YOLOv7 method is used for the process of OD to identify, locate, and classify objects within an image. Furthermore, the MobileNetV3 model is utilized for the feature extraction process. The temporal convolutional network (TCN) model is implemented for classification. Finally, the hyperparameter selection of the TCN model is implemented by the sparrow search optimization algorithm (SSOA) model. The efficiency of the ODSDP-ADLMSSO method is examined under the Indoor OD dataset. The comparison study of the ODSDP-ADLMSSO method demonstrated a superior accuracy value of 99.57% over existing techniques.
Keywords:
Pedestrian
Disabled people
Deep learning
Sparrow search optimization
MobileNetV3
Pathway
Journal
IF:
3.9
Papers:
27.4W
Citations:
83.5W
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