A Hybrid Model for Detection and Classification of Fishing Activity: A Context-Based Approach

Fishing activity matters to the entire world because it affects the economy, ecosystems, and human sustainability. Detecting and classifying fishing activity is a challenge that has been the focus of some studies over the years, but many of them are limited to using solutions without considering context information. These works show solid classification results but are limited to the classification task only. In general, most of them assume a fishing activity is in progress. Hence, these works have not explored the potential of false fishing detections, leading to misclassifications and forcing the fitting of detections into one type of fishing technique. Our hypothesis is that geographic context information can improve the detection of fishing activity and the classification of different types of fishing. Therefore, individual and collective information were extracted from a public labeled database used in related works. Individual information is the kinematics of each vessel, while collective information is the geographic fishing areas. The model adopts a stacking ensemble strategy, with the first level being a kinematic classifier and the second level a correlation model with geographic context. The solution presented effectively fills the identified gaps and demonstrates robust results.