DMN-YOLO：一种用于无人机草原图像中多物种动物检测的轻量级小目标检测器
DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery.
摘要(中文译文)
为满足无人机放牧监测中多类动物精准检测与模型轻量化的需求，本研究提出了DMN-YOLO，一种基于YOLO11n构建的高效检测器。具体而言，引入轻量级下采样模块DSDown，以缓解复杂草地背景下微小目标在下采样过程中细节特征的丢失，从而改善边缘、纹理及局部结构信息的保留。同时，设计了MACFPN多尺度特征融合结构，以应对多类动物目标间的大尺度变化与特征混淆问题，增强跨尺度特征交互与背景抑制，从而获得更好的小目标表征。此外，NWDR Loss结合了CIoU几何约束、归一化Wasserstein距离与自适应加权策略，以提高小目标边界框回归的整体稳定性与定位精度。结果表明，DMN-YOLO在无人机动物检测数据集上达到93.6%的精确率、89.9%的召回率和95.8%的mAP@0.5。与YOLO11n相比，其参数量减少35.7%，模型大小降低29.3%。这些结果表明，DMN-YOLO在保持较强检测性能的同时有效降低了模型复杂度，展现出良好的实际野外部署潜力。
Abstract
To meet the requirements of accurate multi-class animal detection and model lightweighting in UAV-based grazing monitoring, this study presents DMN-YOLO, an efficient detector built upon YOLO11n. In particular, a lightweight downsampling module, DSDown, is introduced to alleviate the loss of detailed features of tiny targets during downsampling under complex grassland backgrounds, thereby improving the preservation of edge, texture, and local structural information. Meanwhile, a MACFPN multi-scale feature fusion structure is designed to handle large scale variations and feature confusion among multiple animal targets, enhancing cross-scale feature interaction and background suppression for better small-target representation. In addition, NWDR Loss combines CIoU geometric constraints, normalized Wasserstein distance, and an adaptive weighting strategy to improve overall stability and localization accuracy of small-target bounding box regression. Results indicate that DMN-YOLO attains 93.6% precision, 89.9% recall, and 95.8% mAP@0.5 on the UAV animal detection dataset. Compared with YOLO11n, it reduces the parameter count by 35.7% while lowering the model size by 29.3%. These results show that DMN-YOLO effectively reduces model complexity while maintaining strong detection performance, demonstrating good potential for practical field deployment.
如何引用
AMA
Qian Huang, Jun Yang, Mengqi Yang, Dan Jiang, Tan Wang. DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery.. Animals : an open access journal from MDPI. 2026; doi:10.3390/ani16111643.
APA
Qian Huang, Jun Yang, Mengqi Yang, Dan Jiang, Tan Wang (2026). DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery.. Animals : an open access journal from MDPI. https://doi.org/10.3390/ani16111643
GB/T 7714
Qian Huang, Jun Yang, Mengqi Yang, Dan Jiang, Tan Wang. DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery.[J]. Animals : an open access journal from MDPI, 2026 doi:10.3390/ani16111643.
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