Abstract:

Aiming at the detection of small target Foreign Object Debris（FOD） on airfield pavement, a FOD target detection algorithm based on improved YOLOv3 is proposed. Firstly, based on YOLOv3 network, Darknet-49 with lower computational complexity is used as the feature extraction network, and the detection scale of YOLOv3 is increased from 3 to 4 to make full use of the shallow feature information. Secondly, the [K]-means++ algorithm based on Markov Chain Monte Carlo sampling （MCMC） is used to cluster analysis on the labeled bounding box size information of FOD, so as to obtain more reasonable sizes for anchor boxes. Finally, the GIoU loss is introduced as the bounding box regression loss function for training on the FOD dataset. The experimental results show that the precision and recall rate of the improved YOLOv3 target detection algorithm reach 95.3% and 91.1%. Compared with Faster R-CNN, it has a higher detection speed. Compared with SSD, it has a higher detection accuracy. And it effectively solves the problem of missing detection and low positioning accuracy existing in the original YOLOv3.

Key words: Foreign Object Debris（FOD）, small target detection, feature fusion, clustering analysis, loss function

摘要：

针对机场跑道异物（Foreign Object Debris，FOD）的小目标特点，提出一种基于改进YOLOv3的FOD目标检测算法。以YOLOv3网络为基础，采用运算复杂度相对更低的Darknet-49作为特征提取网络，并将检测尺度增加至4个，进行多尺度特征融合。使用基于马尔科夫链蒙特卡罗采样（Markov Chain Monte Carlo sampling，MCMC）的[K]-means++算法对标注边界框尺寸信息进行聚类分析。训练时引入GIoU边界框回归损失函数。实验结果表明，改进的YOLOv3目标检测算法在满足实时性要求的情况下，精确率和召回率达到了95.3%和91.1%，与Faster R-CNN相比具有更高的检测速度，与SSD相比具有更高的检测精度，有效解决了原YOLOv3存在的定位精度偏低和漏检问题。

关键词: 机场跑道异物, 小目标检测, 特征融合, 聚类分析, 损失函数