Deep convolutional neural network with Kalman filter based objected tracking and detection in underwater communications

Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment. As a result, it is essential for there to be underwater exploration. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. This allows for clearer pictures to be seen. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This method combines the max-RGB and shade-of-grey approaches to improve underwater visibility and to train the plotting association necessary to obtain the lighting plot. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. The purpose of this investigation is to present a Kalman Filter (KF) method as a solution to the difficulties associated with underwater communication in terms of object tracking and detection. We were able to separate a section of the object by employing a threshold segment and morphological technique. This allowed us to investigate the invariant moment and area properties of the section. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Results from the simulations show that the suggested model DCNN-KF does a better job of localization than the most advanced methods at the time of the study.