![]() In Proceedings of the 19th international conference on control, automation and systems (pp. Data augmentation method for object detection in underwater environments. Improved metaheuristic-driven energy-aware cluster-based routing scheme for IoT-assisted wireless sensor networks. ![]() Lakshmanna, K., Subramani, N., Alotaibi, Y., Alghamdi, S., Khalafand, O. Wireless Communications and Mobile Computing, 2022, 1–12. Capsule network-based deep transfer learning model for face recognition. D., Neelakandan, S., Chandrasekaran, S., Walia, R., & Martinson, E. Study on the classification performance of underwater sonar image classification based on convolutional neural networks for detecting a submerged human body. Journal of Ocean Engineering and Technology, 34, 277–284. Underwater acoustic research trends with machine learning: Active SONAR applications. Active learning for recognition of shipwreck target in side-scan sonar image. Zhu, B., Wang, X., Chu, Z., Yang, Y., & Shi, J. ![]() Computers and Electrical Engineering, 102, 108135. ![]() Design of fuzzy logic-based energy management and traffic predictive model for cyber physical systems. Fish detection and species classification in underwater environments using deep learning with temporal information. Jalal, A., Salman, A., Mian, A., Shortis, M., & Shafait, F. 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. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. This allowed us to investigate the invariant moment and area properties of the section. We were able to separate a section of the object by employing a threshold segment and morphological technique. 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. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. 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. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This allows for clearer pictures to be seen. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. As a result, it is essential for there to be underwater exploration. Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment.
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