  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.
