@article{Zhang2026, 
author = {Wenbo Zhang and Yijue Jiang and Wei Song and Qi He and Wenbo Zhang},
title = {CAGE-YOLO: A Dense Small Object Detection Model for Aquaculture Net Cages Based on Remote Sensing Images},
year = {2026},
journal = {Smart Agriculture},
volume = {8},
number = {2},
pages = {98-117},
keywords = {aquaculture net cage, small object detection, adaptive dense perception algorithm, enhancement spatial pyramid pooling, mix attention block},
url = {https://www.sciopen.com/article/10.12133/j.smartag.SA202508023},
doi = {10.12133/j.smartag.SA202508023},
abstract = {ObjectiveDetecting dense and small aquaculture net cages in complex backgrounds is difficult, the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management.MethodsA dataset of aquaculture net cages was constructed using high-resolution remote sensing imagery collected from seven representative farming regions (Australia, Canada, Chile, Croatia, Greece, China, and the Faroe Islands), and Cage-YOLO, a deep learning model based on YOLOv5, was proposed for detecting dense and small aquaculture net cages. First, an adaptive dense perception algorithm was introduced, which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages. Second, an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities. Finally, a mixed attention block was incorporated to further enhance the model's perception of dense and small objects.Results and DiscussionsExperimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision, recall, and mean average precision by 5.6, 21.8, and 17.4 percentage points, respectively. The model size was maintained at 16.9 MB, demonstrating both strong performance and deployment advantages.ConclusionsThis study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.}
}