FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

TL;DR

This paper introduces FAD-SAR, a deep learning-based system for detecting fishing activities in SAR images, addressing limitations of traditional methods and improving detection accuracy using advanced models and enhancement techniques.

Contribution

It presents a novel deep learning framework for fishing activity detection in SAR images, utilizing multiple object detection models and enhancement strategies to improve performance.

Findings

Faster R-CNN with OHEM improves Avg-F1 from 0.212 to 0.216

Deep learning models outperform traditional SAR analysis methods

Enhanced detection accuracy in IUU fishing scenarios

Abstract

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.