An Optimized Object Detection Algorithm for Marine Remote Sensing Images

Yougui Ren, Jialu Li, Yubin Bao (), Zhibin Zhao and Ge Yu
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Yougui Ren: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Jialu Li: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Yubin Bao: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Zhibin Zhao: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
Ge Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Mathematics, 2024, vol. 12, issue 17, 1-17

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.

Keywords: deep learning; remote sensing images; object detection; feature extraction network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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