Scholarlink

<p>The timely and accurate detection of unidentified drones is crucial for public safety. However, challenges arise due to background noise in complex environments and limited feature representation of small, distant targets. Additionally, deep learning algorithms often demand substantial computational resources, limiting their use on low-capacity platforms. To address these issues, we propose LMWP-YOLO, a lightweight drone detection method that incorporates a multidimensional collaborative attention mechanism and multi-scale fusion. Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. An optimized loss function refines bounding box matching for small targets, while a pruning strategy removes redundant filters, boosting computational efficiency. Experimental results show that LMWP-YOLO outperforms YOLO11n, with a 22.07% increase in mAP and a 52.51% reduction in parameters. The model demonstrates strong cross-dataset generalization, balancing accuracy and efficiency. These findings contribute to advancements in small drone target detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improved YOLO for long range detection of small drones

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

查看全文
  • Sicheng Zhou,
  • Lei Yang,
  • Huiting Liu,
  • Chongqin Zhou,
  • Jiacheng Liu,
  • Yang Wang,
  • Shuai Zhao,
  • Keyi Wang

摘要

The timely and accurate detection of unidentified drones is crucial for public safety. However, challenges arise due to background noise in complex environments and limited feature representation of small, distant targets. Additionally, deep learning algorithms often demand substantial computational resources, limiting their use on low-capacity platforms. To address these issues, we propose LMWP-YOLO, a lightweight drone detection method that incorporates a multidimensional collaborative attention mechanism and multi-scale fusion. Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. An optimized loss function refines bounding box matching for small targets, while a pruning strategy removes redundant filters, boosting computational efficiency. Experimental results show that LMWP-YOLO outperforms YOLO11n, with a 22.07% increase in mAP and a 52.51% reduction in parameters. The model demonstrates strong cross-dataset generalization, balancing accuracy and efficiency. These findings contribute to advancements in small drone target detection.