Wheat-YOLO: A Real-Time and High Precision Object Detection for Wheat

In the wheat detection work, the wheat is located in a complex environment, weeds and dried leaves will hinder the detection of wheat, the wheat images of shadows, wheat obscuring each other and other phenomena will lead to a reduction in the accuracy of the detection. At the same time, for the small object detection problem, most of the detection algorithms use the detection speed as a cost to improve the detection accuracy, and cannot do a good job of balancing between the two. To address the above issues, this paper proposes an improved YOLO detection algorithm, which aims to improve the detection accuracy of small targets in complex environments while minimising the cost of detection speed. Firstly, two attention mechanisms for small target detection are added to the backbone of YOLOv8; secondly, the target detection header with unified attention is used in the head part, which improves the expressive power of the detection header without any computational expense; and finally, the Inner-MPDIoU function, which is a combination of MPDIoU and Inner ideas, is used as the localisation loss in loss functions.Extensive experiments on publicly available datasets show that the results of the network in this paper are improved in terms of both speed and accuracy compared to the original YOLOv8, enabling a better balance between detection accuracy and speed.