Affiliation:
1. School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Abstract
In response to the challenges posed by small objects in remote sensing images, such as low resolution, complex backgrounds, and severe occlusions, this paper proposes a lightweight improved model based on YOLOv8n. During the detection of small objects, the feature fusion part of the YOLOv8n algorithm retrieves relatively fewer features of small objects from the backbone network compared to large objects, resulting in low detection accuracy for small objects. To address this issue, firstly, this paper adds a dedicated small object detection layer in the feature fusion network to better integrate the features of small objects into the feature fusion part of the model. Secondly, the SSFF module is introduced to facilitate multi-scale feature fusion, enabling the model to capture more gradient paths and further improve accuracy while reducing model parameters. Finally, the HPANet structure is proposed, replacing the Path Aggregation Network with HPANet. Compared to the original YOLOv8n algorithm, the recognition accuracy of mAP@0.5 on the VisDrone data set and the AI-TOD data set has increased by 14.3% and 17.9%, respectively, while the recognition accuracy of mAP@0.5:0.95 has increased by 17.1% and 19.8%, respectively. The proposed method reduces the parameter count by 33% and the model size by 31.7% compared to the original model. Experimental results demonstrate that the proposed method can quickly and accurately identify small objects in complex backgrounds.
Reference36 articles.
1. Zhang, Z. (2005). A Study on Harbor Target Recognition in High Resolution Optical Remote Sensing Image, University of Science and Technology of China.
2. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya;Shugar;Science,2021
3. Sentinel-1 soil moisture at 1 km resolution: A validation study;Balenzano;Remote Sens. Environ.,2021
4. A web-based system for satellite-based high-resolution global soil moisture maps;Khazaei;Comput. Geosci.,2023
5. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context, Springer.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献