Improved detection of high altitude aerial images based on YOLOv5

DOI:	10.11809/bqzbgcxb2023.01.037
Keywords:	YOLOv5; high altitude aerial images; object detection; multi scale feature fusion
Abstract:	YOLOv5 has better performance in object detection of common scene images, but it has a poor performance in detecting objects in high altitude aerial images. Aiming at this problem, this paper proposes an improved YOLOv5 model. Firstly, a target dataset of high altitude aerial images is built to make up for the shortage of such images to train the model. Secondly, multi scale detail enhancement is used to process data images to improve the overall quality of the data. Finally, multi scale feature fusion is used to better balance the object features and location information, and the large scale detection head is added to improve the small object detection ability. The experimental results show that the average accuracy, accuracy rate and recall rate of this method are 12.6%, 10.3% and 6% higher than that of the YOLOv5 model respectively, which meets the detection requirements.
Published:	2023-01-28
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