Abstract
Underwater object detection (UOD) is essential in maritime environmental study and underwater species protection. The development of associated technology holds real-world importance. While current object recognition methods have attained an outstanding performance on terrestrial, they are less suitable in underwater conditions because of dual restrictions: the underwater objects are generally smaller, closely spread, and disposed to obstruction features, and underwater embedding tools have temporary storing and computation abilities. Image-based UOD has progressed fast recently, in addition to deep learning (DL) applications and development in computer vision (CV). Investigators utilize DL models to identify possible objects inside an image. Convolutional neural network (CNN) is the major technique of DL, which enhances the learning qualities. In this manuscript, an Underwater Object Detection and Classification Utilizing the Ensemble Deep Learning Approach and Hybrid Optimization Algorithms (UODC-EDLHOA) technique is developed. The UODC-EDLHOA technique mainly detects and classifies underwater objects using advanced DL and hyperparameter models. Initially, the UODC-EDLHOA model involved several levels of pre-processing and noise removal to improve the clearness of the underwater images. The backbone of EfficientNetB7, which has an attention mechanism, is employed for feature extraction. Furthermore, the YOLOv9-based object detection is utilized. For underwater object detection, an ensemble of three techniques, namely deep neural network (DNN), deep belief network (DBN), and long short-term memory (LSTM), is implemented. Finally, the hyperparameter selection uses the hybrid Siberian tiger and sand cat swarm optimization (STSC) methods. Extensive experimentation is conducted on the UOD dataset to illustrate the robust classification performance of the UODC-EDLHOA model. The performance validation of the UODC-EDLHOA model portrayed a superior accuracy value of 92.78% over existing techniques.