Few-shot Object Detection for Plateau Wildlife Images

Document Type

Article

Publication Date

10-19-2021

Identifier/URL

40970892 (Pure)

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Abstract

For few-shot object detection has attracted wide attention. Plateau wildlife detection is a typical problem of few-shot object detection. The solution of this problem can reduce the manpower and material resources of collecting and marking large-scale wildlife data sets. Deep neural network can significantly improve the efficiency of wildlife detection, and the key step is to obtain a large amount of data with bounding box annotations. However, there are few plateau wildlife pictures with labeling, which is insufficient to support the training of deep neural network. In this paper, we propose a target detection method based on few-shot learning to detect plateau wildlife images, where each animal category has only a small number of annotated bounding boxes (no more than 10). we use two-stage training method to solve the task of small samples detection task. Based on the two-stage target detector Faster R-CNN, we established a few shot target detection model. The training is divided into two stages. The first stage is to train the model on the basic set, and the second stage is to adjust the balance set of the new class and the base class on the last layer. In order to further improve the detection effect, the two-stage training adopts data enhancement methods to expand the sample data. Experimental results show that this model can achieve better detection effect on plateau wildlife pictures with a small number of labeled samples.

DOI

10.1109/IRCE53649.2021.9570930

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