Netting Damage Detection for Marine Aquaculture Facilities Based on Improved Mask R-CNN

Netting damage limits the safe development of marine aquaculture. In order to identify and locate damaged netting accurately, we propose a detection method using an improved Mask R-CNN. We create an image dataset of different kinds of damage from a mix of conditions and enhance it by data augmentation. We then introduce the Recursive Feature Pyramid (RFP) and Deformable Convolution Network (DCN) structures into the learning framework to optimize the basic backbone for a marine environment and build a feature map with both high-level semantic and low-level localization information of the network. This modification solves the problem of poor detection performance in damaged nets with small and irregular damage. Experimental results show that these changes improve the average precision of the model significantly, to 94.48%, which is 7.86% higher than the original method. The enhanced model performs rapidly, with a missing rate of about 7.12% and a detection period of 4.74 frames per second. Compared with traditional image processing methods, the proposed netting damage detection model is robust and better balances detection precision and speed. Our method provides an effective solution for detecting netting damage in marine aquaculture environments

A Universal Aquaculture Environmental Anomaly Monitoring System

The current aquaculture environment anomaly monitoring system is limited in function, making it difficult to provide overall technical support for the sustainable development of aquaculture ecosystems. This paper designs a set for an IoT-based aquaculture environment monitoring device. The device is capable of collecting five aquaculture environment factors such as water temperature, pH, salinity, dissolved oxygen and light intensity throughout the day by wireless data transmission via 4G DTU with a communication success rate of 92.08%. A detection method based on time series sliding window density clustering (STW-DBSCAN) is proposed for anomaly detection, using the confidence interval distance radius of slope to extract subsequence timing features and identify the suspected abnormal subsequences and then further determine the anomalous value by the DBSCAN clustering method. The detection results show that the algorithm can accurately identify abnormal subsequences and outliers, and the accuracy, recall and F1-Score are 87.71%, 82.58% and 85.06%, respectively, which verifies the usability of the proposed method. Further, a fuzzy control algorithm is adopted to specify the warning information, and a software platform is developed based on data visualization. The platform uses WebSocket technology to interact with the server, and combined with the surveillance camera, it can monitor the aquaculture environment and perform data monitoring and analysis in a real-time, accurate and comprehensive manner, which can provide theoretical reference and technical support for sustainable development of aquaculture

Netting Damage Detection for Marine Aquaculture Facilities Based on Improved Mask R-CNN

Netting damage limits the safe development of marine aquaculture. In order to identify and locate damaged netting accurately, we propose a detection method using an improved Mask R-CNN. We create an image dataset of different kinds of damage from a mix of conditions and enhance it by data augmentation. We then introduce the Recursive Feature Pyramid (RFP) and Deformable Convolution Network (DCN) structures into the learning framework to optimize the basic backbone for a marine environment and build a feature map with both high-level semantic and low-level localization information of the network. This modification solves the problem of poor detection performance in damaged nets with small and irregular damage. Experimental results show that these changes improve the average precision of the model significantly, to 94.48%, which is 7.86% higher than the original method. The enhanced model performs rapidly, with a missing rate of about 7.12% and a detection period of 4.74 frames per second. Compared with traditional image processing methods, the proposed netting damage detection model is robust and better balances detection precision and speed. Our method provides an effective solution for detecting netting damage in marine aquaculture environments

Dynamic Behavior of the Net of a Pile–Net-Gapped Enclosure Aquaculture Facility

A pile–net enclosure aquaculture facility, deployed in inshore waters, is a sustainable and ecological aquaculture pattern for rearing fish and other aquatic animals of economic value in China. It is essential to study the maximum force on and deformation of the net system of a pile–net enclosure facility to prevent its failure, since successful aquaculture is highly dependent on the longevity of the net system. In this study, a pile-net enclosure aquaculture facility with a gapped pile-net configuration was numerically investigated based on the lumped mass model. A Newton’s second-law-based motion equation was solved using Euler’s method. Finally, MATLAB was used to visualize the results. The results highlight that the force of a net system significantly increases with ocean loads, and the load of the entire net is mainly from the top half of the net. Moreover, the maximum force of the vertical rope occurs at the connection of the top channel steel. The maximum force of the horizontal rope and net twine occur in the rope near the still-water level and at the connection of the top channel steel, respectively. Thus, the net at those positions should be reinforced to prevent its failure

Dynamic Response Analysis of Anchor Piles for Marine Aquaculture under Cyclic Loading

The anchor pile is widely used in marine aquaculture, and its uplift resistance capacity determines the safety performance of the marine aquaculture structure. Cyclic loads such as wind, waves, and currents in the marine environment affect the uplift resistance capacity of anchor piles. By carrying out a cyclic loading model test of anchor piles for marine aquaculture, the influence of loading amplitude, initial tension angle, and other factors on the uplift resistance of anchor piles was investigated. The experimental results showed that with an increase in the loading amplitude, the cumulative displacement and elastic displacement of the anchor pile under vertical and oblique loading increase, and the stiffness of the soil around the anchor piles decreases. The stability of the anchor piles is reduced. When the loading amplitude is the same, with the increase in the initial loading angle, the lateral cumulative displacement of the anchor pile increases. Meanwhile, the vertical cumulative displacement decreases, the stiffness of the soil around the anchor pile decreases, and the stability decreases

Automatic Extraction of Marine Aquaculture Zones from Optical Satellite Images by R³Det with Piecewise Linear Stretching

In recent years, the development of China’s marine aquaculture has brought serious challenges to the marine ecological environment. Therefore, it is significant to classify and extract the aquaculture zone and spatial distribution in order to provide a reference for aquaculture management. However, considering the complex marine aquaculture environment, it is difficult for traditional remote sensing technology and deep learning to achieve a breakthrough in the extraction of large-scale aquaculture zones so far. This study proposes a method based on the combination of piecewise linear stretching and R3Det to classify and extract raft aquaculture and cage aquaculture zones. The grayscale value is changed by piecewise linear stretching to reduce the influence of complex aquaculture backgrounds on the extraction accuracy, to effectively highlight the appearance characteristics of the aquaculture zone, and to improve the image contrast. On this basis, the aquaculture zone is classified and extracted by R3Det. Taking the aquaculture zone of Sansha Bay as the research object, the experimental results showed that the accuracy of R3Det in extracting the number of raft aquaculture and cage aquaculture zones was 98.91% and 97.21%, respectively, and the extraction precision of the area of the aquaculture zone reached 92.08%. The proposed method can classify and extract large-scale marine aquaculture zones more simply and efficiently than common remote sensing techniques