FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
This addresses monitoring challenges for maritime authorities, but it is incremental as it applies existing object detection models with minor enhancements to a specific dataset.
The paper tackles the problem of detecting illegal, unreported, and unregulated (IUU) fishing activities from synthetic aperture radar (SAR) images by proposing a deep learning-based system, achieving an increase in Avg-F1 value from 0.212 to 0.216 using the Faster R-CNN model with Online Hard Example Mining.
Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216.