Paper Info
Title
A Wheat Spike Detection Method In Uav Images Based On Improved Yolov5
Abstract
Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.
Year
DOI
Venue
2021
10.3390/rs13163095
REMOTE SENSING
Keywords
DocType
Volume
wheat spike detection, unmanned aerial vehicle, deep learning, YOLOv5
Journal
13
Issue
Citations 
PageRank 
16
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Jianqing Zhao100.34
Xiaohu Zhang200.68
jiawei yan301.01
Xiaolei Qiu400.34
Xia Yao500.34
Yongchao Tian600.68
Yan Zhu79332.51
Weixing Cao88633.59