Abstract
With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.
Highlights
Object detection from optical remote sensing images plays a vital role in image interpretation
The experimental results of the prototypical network used in the classification process are described, which is followed by the results and an analysis of the chili detection and location process
The watershed segmentation method was used by Fan et al [21] to extract candidate regions, and a convolutional neural network was used in the classification stage
Summary
Object detection from optical remote sensing images plays a vital role in image interpretation. It involves locating and recognizing objects of interest from a given image. With the development of UAV technology, it has become easier for people to obtain high-resolution aerial images, which play an important role in agricultural applications [7,8,9,10,11]. A model trained on a natural scene image dataset may give poor results when used for aerial images. Only a few aerial image datasets are available for crop detection, and they do not contain the images of the required crop species. Studying the recognition of specific crops from aerial images is still meaningful
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