Ma Yukai, Xi Caiping, Ma Ting, Sun Han, Lu Huiyang, Xu Xiang, Xu Chen
College of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
School of Integrated Circuits, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2025 Aug 7;25(15):4857. doi: 10.3390/s25154857.
UAV small target detection in urban security, disaster monitoring, agricultural inspection, and other fields faces the challenge of increasing accuracy and real-time requirements. However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. Therefore, this paper proposes a lightweight algorithm, I-YOLOv11n, based on YOLOv11n, which is systematically improved in terms of both feature enhancement and structure compression. The RFCBAMConv module that combines deformable convolution and channel-spatial attention is designed to adjust the receptive field and strengthen the edge features dynamically. The multiscale pyramid of STCMSP context and the lightweight Transformer-DyHead hybrid detection head are designed by combining the multiscale hole feature pyramid (DFPC), which realizes the cross-scale semantic modeling and adaptive focusing of the target area. A collaborative lightweight strategy is proposed. Firstly, the semantic discrimination ability of the teacher model for small targets is transferred to guide and protect the subsequent compression process by integrating the mixed knowledge distillation of response alignment, feature imitation, and structure maintenance. Secondly, the LAMP-Taylor channel pruning mechanism is used to compress the model redundancy, mainly to protect the key channels sensitive to shallow small targets. Finally, K-means++ anchor frame optimization based on IoU distance is implemented to adapt the feature structure retained after pruning and the scale distribution of small targets of UAV. While significantly reducing the model size (parameter 3.87 M, calculation 14.7 GFLOPs), the detection accuracy of small targets is effectively maintained and improved. Experiments on VisDrone, AI-TOD, and SODA-A datasets show that the mAP@0.5 and mAP@0.5:0.95 of I-YOLOv11n are 7.1% and 4.9% higher than the benchmark model YOLOv11 n, respectively, while maintaining real-time processing capabilities, verifying its comprehensive advantages in accuracy, light weight, and deployment.
无人机在城市安全、灾害监测、农业巡检等领域的小目标检测面临着精度和实时性要求不断提高的挑战。然而，现有检测算法仍存在小目标表征能力弱、计算资源开销大、部署适应性差等问题。因此，本文提出了一种基于YOLOv11n的轻量级算法I-YOLOv11n，在特征增强和结构压缩方面进行了系统改进。设计了结合可变形卷积和通道-空间注意力的RFCBAMConv模块，动态调整感受野并强化边缘特征。通过结合多尺度空洞特征金字塔（DFPC）设计了STCMSP上下文的多尺度金字塔和轻量级Transformer-DyHead混合检测头，实现了目标区域的跨尺度语义建模和自适应聚焦。提出了一种协同轻量级策略。首先，通过整合响应对齐、特征模仿和结构维护的混合知识蒸馏，将教师模型对小目标的语义判别能力进行转移，以指导和保护后续的压缩过程。其次，使用LAMP-Taylor通道剪枝机制压缩模型冗余，主要保护对浅层小目标敏感的关键通道。最后，基于IoU距离实现K-means++锚框优化，以适应剪枝后保留的特征结构和无人机小目标的尺度分布。在显著减小模型大小（参数3.87M，计算量14.7GFLOPs）的同时，有效保持并提高了小目标的检测精度。在VisDrone、AI-TOD和SODA-A数据集上的实验表明，I-YOLOv11n的mAP@0.5和mAP@0.5:0.95分别比基准模型YOLOv11n高7.1%和4.9%，同时保持了实时处理能力，验证了其在精度、轻量级和部署方面的综合优势。
