Hybridization of Deep Convolutional Neural Network for Underwater Object Detection and Tracking Model

Abstract

In this present work, underwater object detection and tracking was studied using the efficient Hybridization of Deep Convolutional Neural Network for Underwater Object Detection and Tracking (HDCNN-UODT) model for three bench mark data sets namely UOT32, brackish, and URPC 2020 datasets. The HDCNN-UODT technique primarily employs data augmentation process for increasing the size of the training dataset to improve the detection and average precision. Besides, a hybridization of two Deep Learning (DL) models namely RetinaNet and EfficientNet models was applied as feature extractors. In addition, the bounding box prediction process takes place via support vector regression (SVR) followed by kernel extreme learning machine (KELM) model. The novelty of the present work was demonstrated by the design of SVR based bounding box regression and fusion based feature extractions. The average precision examination (APE), average success rate(ASR) & average frame per second(AFPS) analysis of the HDCNN-UODT model using UOT32 dataset showed better APE (51.27%), ASR (43.19) & AFPS (310.25) over other reported techniques. Additionally, the detection accuracy of the HDCNN-UODT model for brackish and URPC datasets showed improved accuracies for different objects over YOLO v4, T-YOLO v4, and AFFM-YOLO v4 techniques. Moreover, using brackish dataset, HDCNN-UODT model showed highest accuracy of 94.85% for ‘Crab’ object and for URPC dataset, a highest accuracy of 88.34% for ‘Scallop’ object was obtained. Hence based on our outcome, HDCNN-UODT technique might be better suited for the object detection and tracking application.