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Updated: Sep 17, 2025
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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually
Mahir Mohammed Sharif Adam1, Hussah Nasser AlEisa2, Samah Al Zanin3,4
1Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia. m.adam@psau.edu.sa.
Scientific Reports
|July 2, 2025
Summary
This study introduces a new object detection model using advanced deep learning and the Marine Predator Algorithm (MPA) to improve independence for visually impaired individuals. The model achieves 99.63% accuracy in detecting objects, significantly aiding accessibility.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
- Artificial Intelligence
Background:
- Visually impaired individuals face significant challenges with independence due to vision limitations.
- Object detection is crucial for daily tasks and navigation for the visually impaired.
- Existing computer vision models require enhancement for robust object detection in real-world scenarios.
Purpose of the Study:
- To propose a novel Advanced Object Detection for Smart Accessibility using the Marine Predator Algorithm to aid visually challenged people (AODSA-MPAVCP) model.
- To enhance object detection capabilities for improved accessibility and independence for visually impaired individuals.
- To optimize deep learning models for object detection using metaheuristic algorithms.
Main Methods:
- Image pre-processing using adaptive bilateral filtering (ABF) to reduce noise.
- Object detection utilizing the YOLOv10 model.
- Feature extraction with the VGG19 method.
- Classification using a deep belief network (DBN).
- Hyperparameter optimization of the DBN via the Marine Predator Algorithm (MPA).
Main Results:
- The AODSA-MPAVCP model demonstrated superior performance in object detection tasks.
- Experimental evaluation on the Indoor object detection dataset yielded an accuracy of 99.63%.
- The proposed model significantly outperforms existing object detection approaches.
Conclusions:
- The AODSA-MPAVCP model offers a promising solution for enhancing object detection for the visually impaired.
- The integration of MPA for hyperparameter tuning effectively optimizes DBN classification performance.
- This research contributes to advancements in smart accessibility technologies for disabled individuals.
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