Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors developed a new method to find small fishing boats at night along India's west coast using special satellite images that capture nighttime lights. They created a dual-branch deep learning model called YOLO11 that combines two types of images to better spot these small vessels. Their model was very accurate and found many boats that did not send location signals, called dark vessels. Their study also showed when and where fishing is most active in this region. This work helps improve tracking of fishing activities to support better ocean monitoring.
Nighttime Light ImagerySDGSAT-1 SatelliteDeep LearningYOLO ArchitectureFishing Vessel DetectionAutomatic Identification System (AIS)Maritime SurveillanceDark VesselsSmall Object DetectionSpatio-temporal Analysis
Authors
Shantakar Mohanty, Prasun Kumar Gupta, Raian Vargas Maretto
Abstract
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.