An integration of ensemble deep learning with hybrid optimization approaches for effective underwater object detection and classification model
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Summary
This summary is machine-generated.A new Underwater Object Detection and Classification Utilizing the Ensemble Deep Learning Approach and Hybrid Optimization Algorithms (UODC-EDLHOA) technique enhances maritime study and species protection. This method achieves 92.78% accuracy for underwater object detection.
Area Of Science
- Computer Vision
- Deep Learning
- Marine Biology
Background
- Underwater object detection (UOD) is crucial for marine environmental studies and species protection.
- Existing terrestrial object recognition methods struggle with underwater challenges like small, obstructed, and clustered objects, and limited computational resources.
- Recent advancements in deep learning (DL) and computer vision (CV) have significantly improved image-based UOD.
Purpose Of The Study
- To develop an advanced technique for underwater object detection and classification.
- To address the limitations of current UOD methods in challenging underwater environments.
- To improve the accuracy and robustness of underwater object recognition.
Main Methods
- Proposed the Underwater Object Detection and Classification Utilizing the Ensemble Deep Learning Approach and Hybrid Optimization Algorithms (UODC-EDLHOA) technique.
- Employed EfficientNetB7 with an attention mechanism for feature extraction and YOLOv9 for object detection.
- Utilized an ensemble of deep neural network (DNN), deep belief network (DBN), and long short-term memory (LSTM) for UOD, with hyperparameter tuning via hybrid Siberian tiger and sand cat swarm optimization (STSC).
Main Results
- The UODC-EDLHOA model demonstrated robust classification performance on the UOD dataset.
- Achieved a superior accuracy of 92.78% in underwater object detection and classification.
- Outperformed existing techniques in validation experiments.
Conclusions
- The UODC-EDLHOA technique offers a significant advancement in underwater object detection and classification.
- The hybrid optimization and ensemble deep learning approach effectively addresses underwater imaging challenges.
- This method holds great potential for enhancing maritime environmental monitoring and underwater species protection.