An Optimized Object Detection Algorithm for Marine Remote Sensing Images
-
Published:2024-08-31
Issue:17
Volume:12
Page:2722
-
ISSN:2227-7390
-
Container-title:Mathematics
-
language:en
-
Short-container-title:Mathematics
Author:
Ren Yougui1, Li Jialu1, Bao Yubin1, Zhao Zhibin1, Yu Ge1ORCID
Affiliation:
1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Abstract
In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone network and pyramid balancing structure enables our model to adapt to the characteristics of small-target scenarios. Moreover, the attention mechanism, random anchor re-selection strategy, and the strategy of replacing quantization operations with bilinear interpolation further enhance the model’s detection capability at a low cost. Ablation experiments and comparative experiments show that, with a simple backbone, the algorithm in this paper achieves a mAP of 71.2% on the dataset, an improvement in accuracy of about 10% compared to the Faster R-CNN algorithm.
Funder
National Natural Science Foundation of China
Reference26 articles.
1. A survey on object detection in optical remote sensing images;Han;ISPRS J. Photogramm. Remote Sens.,2016 2. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA. 3. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA. 4. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 5. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
|
|