摘要： 棉花生长期间各类昆虫种群动态直接影响农业决策，精准掌握不同昆虫的种群密度是棉花科学种植与虫害防控的关键前提。在虫害检测任务中，目前的小目标检测算法虽然可以有效地检测到小目标昆虫，但在处理较大的昆虫时往往失效。为此，本研究提出了MSDSR-YOLO(Multi-scale Dynamic Super-Resolution Reconstruction YOLO)目标检测模型，利用图像超分辨率技术与动态卷积的有机结合，在提升小目标检测能力的同时进一步优化对其他尺度目标的检测性能。该模型设计了一种新的特征图超分辨率重建网络SMAR-SRNet（Self-Modulated Attention-Residual Super-Resolution Network）并将其嵌入到YOLOv11模型中配合P5-to-P3的特征融合策略，实现了主干深层特征的精准重建并与原始浅层特征进行跨层级融合，增强了对小目标样本的检测能力以及对局部和非局部特征的捕获能力。然后，本研究将全维动态卷积(ODConv)引入网络的主干和颈部结构，结合C3K2模块构建了C3K2-OD，其通过多维动态卷积核提升了模型捕获丰富上下文线索的能力，增强了网络对多尺度昆虫检测的鲁棒性。最后，本研究构建了一个包含7种不同尺度棉田昆虫的新疆地区黄色粘虫板棉田昆虫数据集XJ-CottonPest2024。实验表明在自建数据集和公开数据集上，MSDSR-YOLO均能达到最优的mAP50值，且对不同尺度昆虫进行对比分析后进一步证明所提网络在以小目标为主、多尺度共存昆虫检测中的优势，有助于在智慧农业领域的应用。

Abstract: Population dynamics of various insects during cotton growth directly impact agricultural decisions, making accurate population density data for different insect types a key basis for scientific cotton farming and pest management. In the pest detection task, Although the current small object detection algorithms can effectively detect small object insects , they often fail when dealing with larger insects. For this reason, this study proposes the MSDSR-YOLO(Multi-scale Dynamic Super-Resolution Reconstruction YOLO) object detection model, which utilizes the organic combination of image super-resolution technology and dynamic convolution to enhance the detection capability of small objects while further optimizing the detection performance of other scale objects. The model designs a new feature map super-resolution reconstruction network named SMAR-SRNet (Self-Modulated Attention-Residual Super-Resolution Network) and embeds it into the YOLOv11 model in conjunction with the P5-to-P3 feature fusion strategy, which realizes the accurate reconstruction of the deep features of the backbone and cross-layer fusion with the original shallow features, and enhances the detection ability of small object samples as well as the capture ability of both local and non-local features. Then, in this paper introduced omni-dimensional dynamic convolution (ODConv) into the backbone and neck structures of the network, and constructed the C3K2-OD module by combining with the C3K2 block, which improves the model's ability to capture rich contextual cues through an omni-dimensional dynamic convolution kernel and enhances the robustness of the network to multi-scale insect detection. Finally, this study constructed a yellow sticky board cotton field insect dataset XJ-CottonPest2024 in Xinjiang region containing seven different scales of cotton field insects. Experiments show that the proposed method achieves the best mAP50 values on both the self-built dataset and public dataset. And the comparative analysis of insect detection effects at different scale, it is further proved that the proposed network has the advantages in insect detection with small objects as the main focus and multi-scale coexistence, which is conducive to its application in the field of smart agriculture.