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基于改進(jìn)Yolov5n的無(wú)人機對地面軍事目標識別算法
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Uav ground military target recognition algorithm based on improved Yolov5n
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    摘要:

    針對目前主流的目標檢測算法在真實(shí)航拍戰場(chǎng)數據背景下識別精度低,誤檢率與漏檢率高等問(wèn)題,對Yolo目標識別算法進(jìn)行了研究,提出一種基于改進(jìn)Yolov5n的輕量化航拍軍事目標檢測模型;首先,采用ECA注意力機制與主干網(wǎng)絡(luò )C3模塊融合,以解決航拍圖像背景復雜且存在相似目標干擾問(wèn)題;其次,引入歸一化高斯瓦薩斯坦距離(NWD)代替CIoU損失函數,提高對模糊小目標的檢測識別;最后,采用GSConv輕量化卷積代替標準卷積,減輕模型重量;經(jīng)過(guò)實(shí)驗測試,改進(jìn)后的算法模型平均檢測精度達到81.5%,提升0.9個(gè)百分點(diǎn),模型大小為3.4MB,減輕0.4MB,識別速度為每秒113幀;實(shí)驗結果表明該模型在輕量化的同時(shí)保持著(zhù)高精度的航拍軍事目標檢測;

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    Aiming at the problems of low recognition accuracy and high false detection rate and missing rate of the current mainstream target detection algorithms in the background of real aerial battlefield data, the Yolo target recognition algorithm is studied,A lightweight aerial military target detection model based on improved Yolov5n is proposed,Firstly, ECA attention mechanism is integrated with C3 module of backbone network to solve the problem of complex background and similar object interference in aerial images.Secondly, the CIoU loss function is replaced by A Normalized Gaussian Wasserstein Distance (NWD) to improve the detection and recognition of fuzzy small targets.Finally, GSConv lightweight convolution is used instead of standard convolution to reduce the weight of the model. Through experimental tests, the average detection accuracy of the improved algorithm model reaches 81.5%, an increase of 0.9%, the model size is 3.4MB, reduced 0.4MB, the recognition speed is 113 frames per second.The experimental results show that the model is lightweight while maintaining high precision aerial military target detection.

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引用本文

王乾勝.基于改進(jìn)Yolov5n的無(wú)人機對地面軍事目標識別算法計算機測量與控制[J].,2024,32(6):189-197.

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歷史
  • 收稿日期:2024-01-23
  • 最后修改日期:2024-02-21
  • 錄用日期:2024-02-22
  • 在線(xiàn)發(fā)布日期: 2024-06-18
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