Illegal, unreported, and unregulated (IUU) fishing seriously affects various
aspects of human life. However, current methods for detecting and monitoring
IUU activities at sea have limitations. While Synthetic Aperture Radar (SAR)
can complement existing vessel detection systems, extracting useful information
from SAR images using traditional methods, especially for IUU fishing
identification, poses challenges. This paper proposes a deep learning-based
system for detecting fishing activities. We implemented this system on the
xView3 dataset using six classical object detection models: Faster R-CNN,
Cascade R-CNN, SSD, RetinaNet, FSAF, and FCOS. We applied improvement methods
to enhance the performance of the Faster R-CNN model. Specifically, training
the Faster R-CNN model using Online Hard Example Mining (OHEM) strategy
improved the Avg-F1 value from 0.212 to 0.216, representing a 1.96%
improvement.

使用基于深度学习的系统和物体检测模型，通过合成孔径雷达技术在检测非法、未报告和无监管捕鱼活动方面取得了显著的改善。

FAD-SAR：基于深度学习方法的合成孔径雷达图像鱼类活动检测系统

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

We present the first approach to volumetric performance capture and
novel-view rendering at real-time speed from monocular video, eliminating the
need for expensive multi-view systems or cumbersome pre-acquisition of a
personalized template model. Our system reconstructs a fully textured 3D human
from each frame by leveraging Pixel-Aligned Implicit Function (PIFu). While
PIFu achieves high-resolution reconstruction in a memory-efficient manner, its
computationally expensive inference prevents us from deploying such a system
for real-time applications. To this end, we propose a novel hierarchical
surface localization algorithm and a direct rendering method without explicitly
extracting surface meshes. By culling unnecessary regions for evaluation in a
coarse-to-fine manner, we successfully accelerate the reconstruction by two
orders of magnitude from the baseline without compromising the quality.
Furthermore, we introduce an Online Hard Example Mining (OHEM) technique that
effectively suppresses failure modes due to the rare occurrence of challenging
examples. We adaptively update the sampling probability of the training data
based on the current reconstruction accuracy, which effectively alleviates
reconstruction artifacts. Our experiments and evaluations demonstrate the
robustness of our system to various challenging angles, illuminations, poses,
and clothing styles. We also show that our approach compares favorably with the
state-of-the-art monocular performance capture. Our proposed approach removes
the need for multi-view studio settings and enables a consumer-accessible
solution for volumetric capture.

本研究通过引入 PIFu 算法和使用表面本地化算法以及在线难例挖掘技术，从而在不需要昂贵的多视图系统或繁琐的预定义模型的情况下，从单眼视频中实时捕捉和渲染了一个完整的实纹三维人体。

单目实时体积表演捕捉

Monocular Real-Time Volumetric Performance Capture

Faster R-CNN is one of the most representative and successful methods for
object detection, and has been becoming increasingly popular in various
objection detection applications. In this report, we propose a robust deep face
detection approach based on Faster R-CNN. In our approach, we exploit several
new techniques including new multi-task loss function design, online hard
example mining, and multi-scale training strategy to improve Faster R-CNN in
multiple aspects. The proposed approach is well suited for face detection, so
we call it Face R-CNN. Extensive experiments are conducted on two most popular
and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate
the superiority of the proposed approach over state-of-the-arts.

提出了一种基于 Faster R-CNN 的深度人脸检测方法，通过新的多任务损失函数设计、在线困难样本挖掘和多尺度训练策略等多种技术手段，名为 Face R-CNN 的方法在 FDDB 和 WIDER FACE 两个人脸检测基准测试中证明了在多方面的优越性。