YOLO-PDS：基于改進的YOLOv11的無人機小目標(biāo)檢測算法
電子技術(shù)應(yīng)用
譚勛瓊，王穎林
長沙理工大學(xué) 物理與電子科學(xué)學(xué)院
摘要： 目標(biāo)檢測在遙感領(lǐng)域中具有廣泛的應(yīng)用前景。盡管目標(biāo)檢測算法在自然圖像中取得了明顯的進展，但這些方法直接應(yīng)用于遙感圖像時仍然面臨諸多挑戰(zhàn)。遙感圖像的背景往往比較復(fù)雜且物體較小，導(dǎo)致前景與背景信息的分布極為不平衡。針對無人機圖像小目標(biāo)和物體遮擋的問題，提出了一種基于風(fēng)車狀卷積（PinwheelConv）改進的無人機小目標(biāo)檢測算法。為了改進模型對小目標(biāo)的檢測效果，在骨干網(wǎng)絡(luò)中使用風(fēng)車狀卷積替換普通卷積來更好地適應(yīng)小目標(biāo)提取特征，同時基于風(fēng)車狀卷積的思想設(shè)計了C2f-PC模塊來替換骨干中的C3k2模塊。為解決無人機圖像中目標(biāo)遮擋嚴(yán)重的問題，創(chuàng)新性地提出了C2f-PDWR模塊來替換頸部網(wǎng)絡(luò)中的C3k2模塊，來增強模型的特征融合能力，同時引入了SEAM（Spatially Enhanced Attention Module）來改善模型對被遮擋物體的檢測效果。最后，基于YOLOv11提出對小目標(biāo)檢測更加高效的YOLO-PDS模型。其在VisDrone2019數(shù)據(jù)集上所提方法較基準(zhǔn)模型YOLOv11檢測方法mAP50提高3.7%以上，召回率提高2.2%以上。
中圖分類號：TP391.4 文獻標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.256845
中文引用格式： 譚勛瓊，王穎林. YOLO-PDS:基于改進的YOLOv11的無人機小目標(biāo)檢測算法[J]. 電子技術(shù)應(yīng)用，2025，51(12)：96-102.
英文引用格式： Tan Xunqiong，Wang Yinglin. YOLO-PDS: a small object detection algorithm for drones based on the improved YOLOv11[J]. Application of Electronic Technique，2025，51(12)：96-102.
中文引用格式： 譚勛瓊，王穎林. YOLO-PDS:基于改進的YOLOv11的無人機小目標(biāo)檢測算法[J]. 電子技術(shù)應(yīng)用，2025，51(12)：96-102.
英文引用格式： Tan Xunqiong，Wang Yinglin. YOLO-PDS: a small object detection algorithm for drones based on the improved YOLOv11[J]. Application of Electronic Technique，2025，51(12)：96-102.
YOLO-PDS： a small object detection algorithm for drones based on the improved YOLOv11
Tan Xunqiong，Wang Yinglin
School of Physics and Electronics， Changsha University of Science and Technology
Abstract： Object detection has broad application prospects in the field of remote sensing. Although object detection algorithms have made significant progress in natural images, these methods still face numerous challenges when directly applied to remote sensing images. The background of remote sensing images is often complex, and the objects are relatively small, which leads to an extremely imbalanced distribution of foreground and background information. To address the issues of small targets and object occlusion in drone images, this paper proposes an improved drone small object detection algorithm based on PinwheelConv. To enhance the model's performance in detecting small objects, the PinwheelConv is used in place of regular convolution in the backbone network, which better adapts to the extraction of small target features. Additionally, a C2f-PC module based on the windmill convolution idea is designed to replace the C3k2 module in the backbone. To address the severe occlusion problem in drone images, this paper innovatively introduces the C2f-PDWR module to replace the C3k2 module in the neck network, enhancing the model's feature fusion capability. Moreover, a Spatially Enhanced Attention Module (SEAM) is incorporated to improve the model's detection of occluded objects. Finally, this paper proposes a more efficient small object detection model, YOLO-PDS, based on YOLOv11. The proposed method improves the mAP50 by over 3.7% and the recall rate by more than 2.2% compared to the baseline YOLOv11 detection method on the VisDrone2019 dataset.
Key words : object detection；YOLOv11；Pinwheel Convolution；multidimensional attention mechanism
引言
隨著無人機的出現(xiàn)，航拍領(lǐng)域經(jīng)歷了深刻的變革。最初設(shè)計用于軍事偵察的無人機現(xiàn)已突破傳統(tǒng)應(yīng)用范圍，成為眾多民用和科研領(lǐng)域中的關(guān)鍵工具。無人機技術(shù)的廣泛應(yīng)用得益于其高分辨率圖像采集能力，這為空間數(shù)據(jù)分析提供了全新的視角。無人機在航拍圖像分析中的應(yīng)用已在多個領(lǐng)域中變得不可或缺。在城市規(guī)劃領(lǐng)域，無人機為智慧城市設(shè)計與管理提供了重要支持，提供了推進可持續(xù)發(fā)展的關(guān)鍵數(shù)據(jù)；在環(huán)境監(jiān)測中，無人機為生態(tài)系統(tǒng)評估和野生動物保護提供了寶貴的洞察。此外，憑借其高度的靈活性和多功能性，無人機在災(zāi)后響應(yīng)與管理中的作用也日益凸顯，能夠迅速評估受災(zāi)區(qū)域。這些多元化的應(yīng)用充分證明了無人機作為多功能工具的巨大潛力，突破了傳統(tǒng)應(yīng)用的邊界。然而，由于無人機圖像具有復(fù)雜的背景、小物體的尺寸以及遮擋問題，仍然存在許多挑戰(zhàn)。因此，小物體檢測已經(jīng)成為該領(lǐng)域一個重要且復(fù)雜的研究重點。
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作者信息：
譚勛瓊，王穎林
（長沙理工大學(xué) 物理與電子科學(xué)學(xué)院，湖南 長沙 410114）
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