RETRACTED: A distributed submerged object detection and classification enhancement with deep learning (Retracted Article)

Research in the autonomous underwater detection system has become rapidly increasing in Ocean Technology. In a recent object detection research study, there a need to enhance the quality, which needs to handle submerged object image processing techniques and a lot of demand to develop an intelligent vision system to improve the Blurred Images and low-quality illumination. Manual research in undersea water leads to more significant pressures and complex environments in cost and workforce. It is necessary to develop a high acceptable autonomous image quality system to upgrade image quality. This paper proposed two approaches: (i) Gray shade and Max-RGB filter techniques to improve image quality. (ii) For optimization and low illumination problem modified Convolution Neural Technique (CNN) incorporated for classification and detection. Moreover, our proposed model has compared with Single-shot Detector (SDD), You Only Look Once (Yolo), Fast RCNN, Faster RCNN to uphold the quality detection found objects. This research article aids to found real-time underwater objects classification and detection. It helps to incorporate an Autonomous operation Vehicle (AOV) underwater research. Our experiment results show detection runs speed as 30 FPs (Frame per second).