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

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