Ocean observation system that involves multiple underwater vehicles and seafloor nodes plays an important role in better learning the ocean, where underwater wireless communication is mandatory for massive data interaction. Optical communication that has wide bandwidth and comprehensive working distance is the preferred method compared to acoustic and other methods. However, the presence of directionality makes the optical method difficult to use especially when the transceiver is equipped on a motive vehicle. In this study, an underwater free-space optical communication method of transmitting information is proposed. Characteristics of underwater optical transmission, as well as the photoelectric signal processing and modulation and demodulation algorithms, are studied and modeled. New approach for realizing underwater free-space optical communication is proposed and simulated. A prototype including a free-space optical transmitter and a receiver is developed; tests in different scenarios were carried out, and the results were observed: (1) by using the minimum number of LEDs, the effect of uniform lighting in space is achieved, and the transmitter coverage reaches 160°. (2) When the power of the transmitter is 10 W and the communication rate is 1 Mbps, the maximum communication distance reaches 13 m.
Underwater detection equipment with fish detection technology has broad application prospects in marine fishery resources exploration and conservation. In this paper, we establish a multi-scale retinex enhancement algorithm and a multi-scale feature-based fish detection model to improve underwater detection accuracy and ensure real-time performance. During image preprocessing, the enhancement algorithm combines the bionic structure of the fish retina and classical retinex theory to filter out underwater environmental noise. The detection model focuses on improving the detection performance on small-size targets using a deep learning method based on a convolutional neural network. We compare our method to current mainstream detection models (Faster R-CNN, RetinaNet, YOLO, SSDetc.), and the proposed model achieves better performance, with a mean Average Precision (mAP) of 78.31% and a mean Miss Rate (mMR) of 54.11% in the open fish image data set. The test results for the data from the field experiment prove the feasibility and stability of our model.
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