期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：7
页码：4421-4432
语种：English
出版社：Elsevier
摘要：The accurate recognition of green fruit is of great significance for the automatic monitoring of the whole process of orchard growth, fruit harvesting, and yield estimation. However, accurate small green fruit detection, affected by many factors such as the unstructured orchard environment, the morphological diversity of target fruits, and multiple-scale fruits, is still an unsolved challenge, especially for small-scale fruits in the night environment. In this paper, we propose an optimized Retinanet-PVTv2 by introducing the gradient harmonizing mechanism to detect small green apple/begonia fruits in the night environment, namely GHFormer-Net. Specifically, PVTv2-B1 based on Transformer is applied as the backbone network to extract feature information from the global receptive, which breaks the limitation that spatial convolution is utilized to extract information from the local area; Next, with the help of FPN, shallow features and high-level features with rich semantic information are incorporated by lateral connections and a top-down structure to generate multi-scale feature maps; Then, a detector of RetinaNet is applied to detect green fruits. To adapt to small apple/begonia fruits detection in the night environment, a gradient harmonizing classification loss (GHM-R Loss) and a gradient harmonizing regression loss (GHM-R Loss) are introduced to improve RetinaNet-PVTv2. The experimental results show that our method achieves 85.2%/61.0% AP and 67.5%/45.2% APS on the benchmark of the NightFruit and Gala datasets respectively, which demonstrates the effectiveness of our method for small green apple/begonia fruits detection.
