Abstract
Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.
Highlights
Almost 37% of the global population depends on fish and fish products for their protein intake [1].Global warming and exponential population growth have largely reduced the food supply from marine environments, and there are concerns about the sustainability of proteins obtained from these marine environments
Markov process to identify three fishing activities for pelagic trawlers. Their hidden Markov model (HMM) defines the observed positions conditioned on the latent variables of fishing ship activities and movement parameters which are modeled by a Bayesian model
This paper proposes a deep-learning gear type identification method that uses a deep neural network model, of which frontbased partsfishing are a combination layer of a convolutional neural deep neural model, of whichnetwork front parts combination of a convolutional neural network and a network fully connected neural andare thea backend is a layer fully connected neural network
Summary
Almost 37% of the global population depends on fish and fish products for their protein intake [1]. Fishing gear type identification allows to estimate fishing type and fish catch and further helps to detect IUU fishing Such activities are valuable for preserving marine resources and preventing from overfishing. The movements of fish schools are affected by environmental factors such as water temperature, pH, dissolved oxygen, and food availability (like micro-plankton) Such environmental data are helpful for fishing gear-type identification of fishing ships. This paper presents a deep-learning based method for fishing gear-type identification which uses both AIS data and marine environmental data. It introduces a preprocessing method for AIS data to handle different messaging intervals, missing messages, and contaminated messages.
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