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<p>In terms of small objects in drone traffic monitoring, problems like insufficient feature representation, serious background interference, and poor multi-scale adaptability are often encountered. Especially when dealing with complex traffic situations, poor context linking between objects as well as poor detection in congested regions are more noticeable. In order to solve the above problems, we put forward an adaptive channel weighted fusion module, which is ACFM. First, we build a multi-scale refinement module, which can do cross-scale feature interaction via a downsampling-upsampling path. It is combined with a residual calibration mechanism to greatly improve both the localization consistency and the detail preservation of small objects over different resolutions of feature maps. And then, a grouped sparse mask attention module is created to reduce background noise through channel grouping and sparse gating techniques in order to enhance the local saliency features of sparse small targets. Finally, we add a channel-wise adaptive weighting by using the global context. Using an α weight generator which can change the contribution of features based on scene complexity, it get rid of traditional fixed combination plans. From the experimental results, we can see that adopting GFL as the detector, ACFM achieves better performance improvements on the widely used VisDrone2019, UAVDT dataset, and the maximum gain in mAP is more than 0.8% and 1.3% higher than the comparison methods. On the AU - AIR dataset, ACFM is still a little bit better than the other 0. 5% mAP, it is still robust in the complicated situation.</p>

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ACFM: adaptive channel weighted fusion algorithm for improving small object detection performance in UAV traffic

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

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  • Shijun Liu,
  • Honghao Zhu,
  • Zhenguo Yuan,
  • Xingfei Zhu,
  • Cheng Guo

摘要

In terms of small objects in drone traffic monitoring, problems like insufficient feature representation, serious background interference, and poor multi-scale adaptability are often encountered. Especially when dealing with complex traffic situations, poor context linking between objects as well as poor detection in congested regions are more noticeable. In order to solve the above problems, we put forward an adaptive channel weighted fusion module, which is ACFM. First, we build a multi-scale refinement module, which can do cross-scale feature interaction via a downsampling-upsampling path. It is combined with a residual calibration mechanism to greatly improve both the localization consistency and the detail preservation of small objects over different resolutions of feature maps. And then, a grouped sparse mask attention module is created to reduce background noise through channel grouping and sparse gating techniques in order to enhance the local saliency features of sparse small targets. Finally, we add a channel-wise adaptive weighting by using the global context. Using an α weight generator which can change the contribution of features based on scene complexity, it get rid of traditional fixed combination plans. From the experimental results, we can see that adopting GFL as the detector, ACFM achieves better performance improvements on the widely used VisDrone2019, UAVDT dataset, and the maximum gain in mAP is more than 0.8% and 1.3% higher than the comparison methods. On the AU - AIR dataset, ACFM is still a little bit better than the other 0. 5% mAP, it is still robust in the complicated situation.