High resolution remote sensing image object detection algorithm based on improved YOLOv8
In view of problems such as objects are interfered by complex background, objects are small and densely distributed, objects are multi-scale and their directions are random in high resolution remote sensing image data, an object detection algorithm for high resolution remote sensing image based on i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
China InfoCom Media Group
2025-01-01
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| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/111998943/ |
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| Summary: | In view of problems such as objects are interfered by complex background, objects are small and densely distributed, objects are multi-scale and their directions are random in high resolution remote sensing image data, an object detection algorithm for high resolution remote sensing image based on improved YOLOv8 was proposed. Firstly, dynamic snake convolution was incorporated to make the algorithm detect objects with different scales and directions better; Secondly, in order to enable the algorithm to capture the global context information in the image with complex background, inverted residual mobile block was combined with Shift-Wise convolution . Attention-based Intra-scale feature interaction was used. Bi-directional feature pyramid network and semantics and detail infusion module were combined as the algorithm's feature fusion network. An improved efficient decoupled detection head was designed. Finally, combining NWD and WIoU loss functions, a new loss function NWD-WIoU was designed to accelerate the convergence speed of algorithm detection and improve the detection performance of the algorithm for small objects. Experimental results on three public remote sensing image object detection datasets show that the high resolution remote sensing image detection algorithm based on improved YOLOv8 performs better than other mainstream high resolution remote sensing image detection algorithms. |
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| ISSN: | 2096-0271 |