A Study of Artificial Neural Network Technology Applied to Image Recognition for Underwater Images

• Published in: IEEE access Vol. 10; pp. 13844 - 13851
• Main Authors: Wu, Bo-Wen, Fang, Yi-Chin, Wen, Chan-Chuan, Chen, Chao-Hsien, Lee, Hsiao-Yi, Chang, Shun-Hsyung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Exploration; Gray-scale; Image analysis; Image processing; image recognition; Image resolution; Image segmentation; Light reflection; Low-pass filters; Magnetic separation; moment invariants; neural network; Neural networks; Neurons; Object recognition; Ocean currents; Schools (fish); Sea vessels; Shape; Shape recognition; Software; Target recognition; Underwater; Underwater exploration; Variance analysis; Water temperature
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3144742

In this study, the researchers developed holographic image software for the Polaris, a nongovernmental Taiwanese oceanographic research vessel. It is a survey vessel that was codeveloped through an industry-academia collaboration between National Kaohsiung University of Science and Technology and Dragon Prince Hydro-Survey Enterprise Co. With a weight of 260 tons, length of 36.98 m, and width of 6.80 m, the vessel can travel at a speed of 11 knots. It has undergone underwater rescue and exploration operations and is therefore fairly experienced in such operations. When performing underwater exploration missions, survey vessels are often faced with interferences caused by factors such as current velocity; water temperature, refraction, and spectral conditions; climate; ocean current; presence of algae; and light reflection from schools of fish. Therefore, instantaneous image analysis is imperative for marine exploration. In accordance with the instantaneous recognition needs of the Polaris, the researchers developed artificial-neural-network-based recognition software for rapidly recognizing the category of a detected underwater object. Recognition of shapes in low-resolution underwater images was improved using a neural network resulting in an average recognition rate of 95%. Analysis of variance also indicated that the neural network yielded a significantly higher recognition rate than did manual recognition.