Computer and Modernization ›› 2024, Vol. 0 ›› Issue (12): 78-83.doi: 10.3969/j.issn.1006-2475.2024.12.012

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SAR Ship Detection Algorithm Based on Improved YOLOv8

  

  1. (Artificial Intelligence Laboratory, Shanghai Aerospace Electronics Technology Research Institute, Shanghai 201109, China)
  • Online:2024-12-31 Published:2024-12-31

Abstract: To enhance the accuracy of ship target detection in SAR images, especially when facing challenges such as uneven target sizes, dense distributions, and complex backgrounds, an improved YOLO-3M ship target detection algorithm based on YOLOv8 is proposed. Firstly, the algorithm introduces a Multi-Scale Dilated Convolution Block (MSDB) into the backbone network, which uses convolutions with different dilation rates to extract multi-scale features, thereby enlarging the receptive field without increasing computational costs. Secondly, a Multidimensional Collaborative Attention (MCA) mechanism is incorporated into the neck network to capture key features across the channel, height, and width dimensions, facilitating interaction between different dimensional information and helping the network to effectively focus on key parts within complex backgrounds. Finally, an MPDIoU loss function is introduced in the detection head to address issues with existing loss functions that struggle to effectively detect when the predicted bounding box and the actual bounding box have the same aspect ratio but completely different widths and heights. Experimental results on the SSDD dataset show that the YOLO-3M algorithm achieves higher accuracy and average precision while effectively reducing the number of parameters and computational requirements, making the model more lightweight and suitable for resource-constrained environments. Additionally, there is a significant improvement in reducing false positives and false negatives in complex ship detection scenarios.

Key words:  , ship detection; SAR image; YOLOv8; multi-scale dilated convolution block; multidimensional collaborative attention; MPDIoU

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