In-situ Detection Method of Jellyfish Based on Improved Faster R-CNN and FP16

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Weihong, Bi, Yun, Jin, Jiaxin, Li, Lingling, Sun, Guangwei, Fu, Wa, Jin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Aquaculture; Computer networks; Convolutional neural networks; Faster R-CNN; Feature extraction; Fisheries; Floating point arithmetic; FP16; High definition; Image contrast; In-situ detection; Jellyfish; Marine environment; Monitoring; Plankton; Residual neural networks; Resnet50; Sea measurements; Sea surface; Software; Toxicology; Training; Underwater
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3300655

In recent years, large numbers of jellyfish have congregated in marine areas, leading to a decline in other plankton and fisheries. Jellyfish themselves have a certain toxicity and aggression, which have a serious impact on the safety of human life. In order to detect the quantity and distribution of underwater jellyfish, and to be more proactive in the prevention and control of Aurelia outbreaks, this study proposed a method for in-situ detection of underwater jellyfish based on the improved Faster R-CNN network model. Firstly, the real data sets of three species of jellyfish in the Qinhuangdao sea area were established by using underwater high-definition camera. The Multi Scale Retinex with Colour Restoration (MSRCR) algorithm was used to improve the brightness and contrast of the underwater images. Secondly, the residual network Resnet50 was integrated into the backbone network for better feature extraction; then the semi-precision floating-point number FP16 was added to improve the training speed. Finally, comparative experiments were conducted to verify the improved network. The F1 value, the P-R curve, the Loss curve and the AP value of the three detection models were evaluated and compared. The experimental results showed that compared to Vgg16 network and YOLO V3 network, the training speed was improved from 1.85bit/s to 7.35bit/s, and the accuracy was also improved to over 0.98. The experimental results were good, and the research results provided a more accurate and faster method for the in-situ detection of underwater jellyfish.