Abstract
At present, two main problems, which are the multi-scale of ship targets and the lightweight of detection models, restrict the real-time and on-orbit detection of ship targets on SAR images. To solve two problems, we propose a lightweight ship detection network (LS-YOLO) based on YOLOv5 model for SAR images. In the proposed network, we propose two modules, namely, Feature Refinement Module (FRM) and DCSP. The FRM module is designed to solve the multi-scale problem of ship targets in SAR images. This structure can effectively expand the receptive field of the model and improve the detection ability of small target ships. DCSP is lightweight module based on YOLOv5 CSP. This module effectively reduces model parameters and computation while keeping feature extraction ability as much as possible. The LS-YOLO detection speed is up to 1.2 ms, the accuracy (AP) is 96.6%, and the model size is only 3.8 MB. It can balance detection accuracy and detection speed, and provide reference for the construction of real-time detection network.
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Acknowledgments
This work was supported in part by the National Science Foundation of China (Grant Nos. 61873335, 61833011); the Project of Science and Technology Commission of Shanghai Municipality, China (Grant Nos. 20ZR1420200, 21SQBS01600, 22JC1401400, 19510750300, 21190780300); and the 111 Project, China under Grant No. D18003.
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He, Y., Li, ZX., Wang, YL. (2022). LS-YOLO: Lightweight SAR Ship Targets Detection Based on Improved YOLOv5. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_6
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