Object Detection for Aircraft Turnover Milestone based on Modified

Abstract

In the target detection task of aircraft turnaround milestone in foggy scenario, there are some problems such as unstable location of prediction frame boundary, high error detection rate and poor detection effect of small target. A new target detection method BTM-YOLO (Broad-sighted upsample and three-dimensional attention multiple detection head YOLO) is proposed, which is based on YOLOv7 network. Add a small target detection head to improve the ability of small target detection; The up-sampling module OVRAFE is introduced to reduce the information loss in the up-sampling process. Replace CIoU with Median Wise IoU (MWIoU) to suppress the problem of poor sample swelling in data sets. The improved model makes up for the performance shortcomings of small target detection in foggy days, and the average detection accuracy on the real foggy day test set is 76.2%, which is 3.32% higher than that of the original model, basically meeting the task requirements.