Abstract
Fishing net cleanliness plays a critical role for aquaculture industry as bio-fouled nets restrict the flow of water through the net leading to a build-up of toxins and reduced oxygen levels within the pen, thereby putting the fish under increased stress. In this paper, we proposed an underwater fishing Net Health State Estimation (NHSE) method, which can automatically analyze the degree of fouling on the net through underwater image analysis using remotely operated vehicles (ROV) images, and calculate a blocking percentage metric of each net opening. The level of fouling estimated through this method help the operators decide on the need of cleaning or maintenance schedule. There are mainly six modules in the proposed NHSE method, namely user interaction, distortion correction, underwater image dehazing, marine growth segmentation, net-opening structure analysis, and blocked percentage estimation. To evaluate the proposed NHSE method, we collected and labeled several underwater images in Mulroy Bay, Ireland with pixel-wise annotations. In order to verify the universality and robustness of the algorithm, we simulated and built a virtual fishing farm, and, on this basis, collected and labeled fishing net images under different environmental conditions. Seven evaluation metrics are introduced to demonstrate the effectiveness and advantages of the proposed method.
Highlights
Developing new cage farming technologies is important for sustainable and economical aquaculture, while fishing net, which is an integral part of cage, plays an essential role in aquaculture
We aim to estimate the health state of fishing net through a more intuitive way, by quantifying visual inspection under water, images are used for fishing net health state estimation, which is more consistent with the way aquaculture professionals obtain standard information
This study aims to address fishing net health estimation problem by an image processing way, so here we talk about the traditional fishing related environment monitoring and related image processing algorithms
Summary
Developing new cage farming technologies is important for sustainable and economical aquaculture, while fishing net, which is an integral part of cage, plays an essential role in aquaculture. There are two ways for cleaning of the fishing net in traditional aquaculture. One way is manually checking the health state of fishing net combined with considering season, temperature and water quality, or cleaning at regular intervals, for instance two weeks in summer. It requires a significant of manpower and material resources whether it is checking or cleaning. Some researchers [1,2,3] focus on designing a monitoring system through different types of sensors, such as water flow sensor, oxygen sensor, or temperature sensor, for combined analyses and subsequent establishment of the current state of the underwater surroundings of fishing net. We aim to estimate the health state of fishing net through a more intuitive way, by quantifying visual inspection under water, images are used for fishing net health state estimation, which is more consistent with the way aquaculture professionals obtain standard information
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