LIGHTWEIGHT TARGET DETECTION FOR UAV AERIAL IMAGE BASED ON OMNI-DIMENSIONAL DYNAMIC CONVOLUTION

Wei Rengan; Feng Shuang; Kong Huafeng

doi:10.3969/j.issn.1000-386x.2024.05.025

Computer Applications and Software > 2024 > 41(5): 158-165,182. > DOI: 10.3969/j.issn.1000-386x.2024.05.025

Wei Rengan, Feng Shuang, Kong Huafeng. LIGHTWEIGHT TARGET DETECTION FOR UAV AERIAL IMAGE BASED ON OMNI-DIMENSIONAL DYNAMIC CONVOLUTION[J]. Computer Applications and Software, 2024, 41(5): 158-165,182. DOI: 10.3969/j.issn.1000-386x.2024.05.025

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LIGHTWEIGHT TARGET DETECTION FOR UAV AERIAL IMAGE BASED ON OMNI-DIMENSIONAL DYNAMIC CONVOLUTION

1.College of Automotive Engineers, Hubei University of Auomolive Technology, Shiyan 442000, Hubei;
2.College ofInformation Engineering, Wuhan Business University, Wuhan 430000, Hubei

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Received Date: December 28, 2023
Available Online: August 03, 2024

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Abstract

Abstract

A lightweight target detection algorithm for UAV aerial images is proposed to address the problems of large model size and low detection accuracy in traditional UAV aerial image target detection.Based on YOLOv5s,a small target detection layer is added,and a omni-dimensional dynamic convolution is used to replace the ordinary convolution,which reduces the number of parameters.Using cross-layer and cross-scale weighted feature fusion,and introducing FasterNet module,the feature extraction capability is strengthened.A dynamic label assignment strategy is used to significantly improve the detection accuracy.The experimental results show that the proposed algorithm outperforms the original YOLOv5s algorithm in terms of accuracy and model volume,and can more efficiently accomplish the task of target detection in UAV aerial images.
- Aerial image ,
- Target detection ,
- Dynamic convolution ,
- Features fusion ,
- Label assignment

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References (25)

References

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