An Underwater Object Detection and Tracking using Bounding Box Prediction and Convolutional Neural Network

Nowadays, an Object detection and tracking stays energetic and inspiring task in present processer imagination with extensive applications in various industries such as investigation, independent robot directions, and vehicle navigation. The increase in availability of computing power and vast public datasets has made it possible to develop and train robust object identification and tracking systems, driving innovation and advancements in these fields of tracking objects. Object detection faces several challenges such as, where objects are partially or fully hidden by other objects, and variation in pose and scale, which affects an object's appearance. Cluttered backgrounds and changing lighting conditions are also make detection difficult. To overcome these issues, the method of Convolutional Neural Network for Underwater (UW) Object Detection and Tracking (CNN-UWODT) is proposed with DenseNet feature extraction and bounding box for tracking the objects. The process involves with collecting input images from UOT-32 dataset converting to frames extracting features with DenseNet; then predicting the bounding boxes with CNN, and classifying objects with Kernel Extreme Learning Machine (KELM) component to identify key metrics from the input data. The proposed CNN-UWODT presents better results than the traditional You Only Look Once, Version 3 (YOLO-V3)in terms of precision ($94 \%$), recall ($96 \%$) and f1 score ($95 \%$) respectively.