基于空间深度转换域自适应学习的小麦赤霉病无监督检测方法OA

When using UAV remote sensing to monitor wheat scab,the size of the object in the captured images decreases as the UAV's flight altitude increases.This makes data annotation more challenging,requiring more time and labor.To address this issue,an unsupervised UAV-based wheat scab detection method was proposed.The method proposed a space-to-depth domain adaptation detection network(SPD-DANet),which learned the knowledge of the source domain data through the adversarial idea and transfered it to the unlabeled target domain thereby realizing unsupervised wheat scab detection.Firstly,to tackle the issue of small scab lesions in UAV-captured wheat images,a spatial-to-depth feature extractor(SPD-FE)that transformed spatial information into depth information was designed.This enabled the network to learn features of small scab objects more effectively.Secondly,using SPD-FE,adversarial-based classification adaptation and bounding box regression adaptation modules for the detection network were constructed.This enabled domain adaptation learning sequentially in classification and bounding box regression steps,leveraging source domain knowledge for unsupervised detection in the target domain.The experimental results showed that the proposed method improved the detection accuracy(AP50)by 5.3～20.5 percentage points compared with other object detection methods such as DETR,YOLO v5,etc.,and performed the best on the unsupervised detection task of wheat scab,and its detection accuracy AP50 was improved by 6.5 percentage points compared with that of the baseline network.The research can provide some support and help for the unsupervised detection of wheat scab.