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This study offers a novel technology strategy to help those who are blind or visually impaired. The objective is to identify and locate things within a living room setting; then convey this data via auditory cues. The approach used AlexNet for precise object detection in addition to a novel technique known as Novel Particle Swarm Optimization. Thirty algorithm iterations and a sample size of forty were employed in the investigation. With 80% power, the significance threshold was established at α = 0.05. An independent sample t-Test revealed a statistically significant difference (p < 0.05) between the two algorithms’ accuracy and loss. Experimental investigation carried out using a Python compiler demonstrated that the Novel Particle Swarm Optimization classifier achieved an accuracy of 95.48%, surpassing the accuracy of AlexNet which was 92.75%. This technology offers a high level of confidence in object detection, and pretrained models are shown to be more efficient in terms of computing time and cost. The system allows for the simultaneous detection of multiple objects, with their identities displayed as text before being spoken. This has the potential to greatly benefit visually impaired individuals by providing additional auditory assistance.

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Improving the Prediction of Obstacles in a Living Room and Naming It to Blind People in Audio Using Particle Swarm Optimization Classifier and AlexNet

  • 会议论文

  • 发表日期：

    2025年

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  • M. Premalatha,
  • R. Bhavani,
  • R. Mahaveerakannan,
  • K. Sudhakar

摘要

This study offers a novel technology strategy to help those who are blind or visually impaired. The objective is to identify and locate things within a living room setting; then convey this data via auditory cues. The approach used AlexNet for precise object detection in addition to a novel technique known as Novel Particle Swarm Optimization. Thirty algorithm iterations and a sample size of forty were employed in the investigation. With 80% power, the significance threshold was established at α = 0.05. An independent sample t-Test revealed a statistically significant difference (p < 0.05) between the two algorithms’ accuracy and loss. Experimental investigation carried out using a Python compiler demonstrated that the Novel Particle Swarm Optimization classifier achieved an accuracy of 95.48%, surpassing the accuracy of AlexNet which was 92.75%. This technology offers a high level of confidence in object detection, and pretrained models are shown to be more efficient in terms of computing time and cost. The system allows for the simultaneous detection of multiple objects, with their identities displayed as text before being spoken. This has the potential to greatly benefit visually impaired individuals by providing additional auditory assistance.