基于YOLOv7的机场跑道异物检测算法

Foreign object debris detection based on YOLOv7 algorithm

机场跑道异物对航班安全起降构成极大威胁,准确及时地检测并清除机场跑道异物是机场安全工作的重点。针对机场跑道异物检测任务中的小目标检测精确度与实时性,提出一种基于YOLOv7的机场跑道异物检测算法。首先在主干网络引入CBAM模块,从空间注意力与和通道注意力两方面专注小目标特征信息提取;其次在加强特征提取网络结合AFPN思想提出SA-PANet结构,将相邻有效特征层进行渐进式特征融合,缓解有效特征层之间的语义差距;然后在加强特征提取网络的PANet结构下采样支路中引入BiFormer模块,聚焦小目标特征信息的进一步融合提取;最后在边界框定位损失函数计算过程中引入MPDIoU Loss,加速模型收敛并提升机场跑道异物检测准确率与定位精度。在机场跑道异物图像数据集上实验表明,改进后算法mAP 50为98.76%,较改进前算法提升9.09个百分点。与其他针对机场跑道异物检测的算法相比,改进后算法具有更高的检测精度同时将模型参数量与模型计算量增幅控制在可接受范围内,达到机场跑道异物检测任务的准确、快速需求。

FOD poses a great threat to airports.Detecting and removing FOD accurately and timely is the key point of airports’safety work.A FOD detection algorithm that based on YOLOv7 is proposed to meet the requirements of accuracy and real-time.Firstly,the CBAM module is introduced into the backbone network to focus on the extraction of small target feature information from two aspects:spatial attention and channel attention.Secondly,the idea of AFPN is integrated into the feature extraction network and SA-PANet structure is proposed in combination with it.SA-PANet can asymptotically fuse adjacent effective feature layers and alleviate the semantic gap between them.Thirdly,the BiFormer module is introduced into the down-sampling branch of PANet,which can focus on further fusion extraction of small target feature information in the feature extraction network.Lastly,MPDIoU Loss is introduced into the boundary frame positioning loss calculation,which can not only accelerate the convergence of the model but also improve the detection accuracy and location accuracy.Experiments on FOD datasets show that the mAP 50 of the improved YOLOv7 algorithm is 98.76%,which is 9.09 percentage points higher than the original YOLOv7.Comparing with other algorithms for FOD detection,the improved YOLOv7 algorithm has higher detection accuracy and the increase of Params and GELOPs is controlled within the acceptable range,which meet the accurate and fast requirements of FOD detection tasks.