ECF-YOLO: An enhanced YOLOv8 algorithm for ship detection in SAR images

Peng Lu; Xinpeng Hao; Wenhui Li; Congqin Yi; Ru Kong; Teng Wang; Peng Lu; Xinpeng Hao; Wenhui Li; Congqin Yi; Ru Kong; Teng Wang

doi:10.3934/era.2025150

Electronic Research Archive

2025, Volume 33, Issue 5: 3394-3409. doi: 10.3934/era.2025150

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ECF-YOLO: An enhanced YOLOv8 algorithm for ship detection in SAR images

1.
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2.
Modern Educational Technology Center, Shanghai Maritime University, Shanghai 201306, China
3.
Shandong Provincial Institute of Land Space Data and Remote Sensing Technology (Shandong Marine Dynamic Monitoring Center), Shandong 250014, China

Received: 26 February 2025 Revised: 01 April 2025 Accepted: 21 April 2025 Published: 29 May 2025

Synthetic aperture radar (SAR) is an advanced microwave sensor widely used in ocean monitoring because of its resilience to light and weather conditions. However, SAR ship detection tends to have relatively low accuracy due to the prevalence of complex backgrounds and small targets in the detection process. To address these issues, we proposed ECF-YOLO, an improved ship detection algorithm based on YOLOv8. The algorithm enhanced the feature extraction ability of the model and reduced the number of parameters and computational cost by developing a novel C2f-EMSCP module, which replaced the original C2f module in the backbone network. Additionally, we proposed the CGFM module in the neck network, which was designed to improve the detection accuracy of small ship targets by selecting features after combining shallow and deep feature maps. Furthermore, the Inner-SIoU loss function was introduced to replace the CIoU, providing a more precise overlap calculation between the target and anchor boxes, thus further improving detection accuracy. The experimental results for the SAR ship detection dataset showed that compared to YOLOv8n, ECF-YOLO improved $ \mathrm{AP_{75}} $ by 2.8% and $ \mathrm{AP_{50:95}} $ by 0.9%. Compared to other mainstream algorithms like YOLOv9t, YOLOv10n, and YOLO11n, ECF-YOLO achieved improvements of 3.4%, 4.6%, and 4.9% for $ \mathrm{AP_{75}} $, and 3.4%, 1.9%, 3.0% for $ \mathrm{AP_{50:95}} $, respectively, demonstrating its effectiveness for detecting small targets.
- YOLOv8,
- ship detection,
- C2f-EMSCP,
- CGFM,
- Inner-SIoU loss function
Citation: Peng Lu, Xinpeng Hao, Wenhui Li, Congqin Yi, Ru Kong, Teng Wang. ECF-YOLO: An enhanced YOLOv8 algorithm for ship detection in SAR images[J]. Electronic Research Archive, 2025, 33(5): 3394-3409. doi: 10.3934/era.2025150

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Abstract

Synthetic aperture radar (SAR) is an advanced microwave sensor widely used in ocean monitoring because of its resilience to light and weather conditions. However, SAR ship detection tends to have relatively low accuracy due to the prevalence of complex backgrounds and small targets in the detection process. To address these issues, we proposed ECF-YOLO, an improved ship detection algorithm based on YOLOv8. The algorithm enhanced the feature extraction ability of the model and reduced the number of parameters and computational cost by developing a novel C2f-EMSCP module, which replaced the original C2f module in the backbone network. Additionally, we proposed the CGFM module in the neck network, which was designed to improve the detection accuracy of small ship targets by selecting features after combining shallow and deep feature maps. Furthermore, the Inner-SIoU loss function was introduced to replace the CIoU, providing a more precise overlap calculation between the target and anchor boxes, thus further improving detection accuracy. The experimental results for the SAR ship detection dataset showed that compared to YOLOv8n, ECF-YOLO improved $ \mathrm{AP_{75}} $ by 2.8% and $ \mathrm{AP_{50:95}} $ by 0.9%. Compared to other mainstream algorithms like YOLOv9t, YOLOv10n, and YOLO11n, ECF-YOLO achieved improvements of 3.4%, 4.6%, and 4.9% for $ \mathrm{AP_{75}} $, and 3.4%, 1.9%, 3.0% for $ \mathrm{AP_{50:95}} $, respectively, demonstrating its effectiveness for detecting small targets.

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