基于深度学习的夜光图像渔船检测及渔业监测
Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images
AI总结：
本研究提出双分支YOLO11模型，利用SDGSAT-1卫星夜光图像检测印度西海岸渔船，检测精度0.99、召回率0.93，发现大量潜在黑船，为海事监视及渔业监管提供支撑。
AI中文摘要：
海事监视的需求催生了对渔船活动监测的需求，尤其是应对未搭载自动识别系统（Automatic Identification System，AIS）传输的“黑船”挑战。本研究提出一种利用SDGSAT-1卫星夜间光（Nighttime Light，NTL）图像结合深度学习技术的新型方法，以提升印度西海岸的渔业监测意识，用于检测小型渔船。研究开发了双分支YOLO11架构，以利用SDGSAT-1卫星的10米全色图像和40米RGB图像；该定制模型架构专门针对NTL图像中的小目标检测进行了优化，具有并行卷积骨干网络，可处理两种模态后再进行拼接以增强特征提取。双分支YOLO11模型表现出最优性能，精度为0.99，召回率为0.93，F1分数为0.96，mAP@50为0.96，显著优于单分支YOLOv5s、YOLOv8s及标准YOLO11s架构。将该模型应用于印度西海岸，在2022-2023年的时间序列数据集中共检测到31525个船舶实例。与AIS数据的交叉匹配分析显示，仅7146艘（22.7%）检测到的船舶有对应的AIS传输，而24379艘（77.3%）被识别为潜在黑船。时空分析显示，1-4月为捕鱼活动高峰期，主要活动走廊位于距海岸线50-100公里范围内，对应于生产力较高的大陆架区域。本研究通过强调夜间光卫星图像在渔船检测中的有效性，为海事监视能力做出了贡献，并提供了印度水域捕鱼模式及潜在监管合规问题的宝贵见解。
英文摘要：
The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of "dark vessels" that operate without Automatic Identification System (AIS) transmission. This study presents a novel approach for detecting small-scale fishing vessels using nighttime light (NTL) imagery from the SDGSAT-1 satellite, combined with deep learning techniques to enhance fishing monitoring awareness along the western coast of India. A dual-branch YOLO11 architecture was developed to exploit both the 10-meter panchromatic and 40-meter RGB imagery from SDGSAT-1. The custom model architecture was specifically optimized for small object detection in NTL imagery, featuring parallel convolutional backbones that process both modalities before concatenation for enhanced feature extraction. The dual-branch YOLO11 model demonstrated optimal performance with a precision of 0.99, recall of 0.93, F1-score of 0.96, and mAP@50 of 0.96, significantly outperforming single-branch implementations of YOLOv5s, YOLOv8s, and standard YOLO11s architectures. When applied to the western coast of India, the model detected 31525 vessel instances across the temporal dataset spanning 2022-23. Cross-matching analysis with AIS data revealed that only 7146 (22.7%) of detected vessels had corresponding AIS transmissions, while 24379 (77.3%) were identified as potential dark vessels. Spatio-temporal analysis showed peak fishing activity during January-April, with a primary activity corridor parallel to the coastline within 50-100 km, corresponding to productive continental shelf areas. This research contributes to maritime surveillance capabilities by highlighting the effectiveness of nighttime lights satellite imagery for fishing vessel detection and provides valuable insights into fishing patterns and potential regulatory compliance issues in Indian waters.
