Abstract
In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.
Highlights
In recent years, flower classification and detection has been of considerable interest to the computer vision community which can be applied in AIoT for the smart garden
Our contributions are summarized as follows: (i) We present a flower detection method, an end-toend deep convolutional neural network for flower detection applied in a smart garden in AIoT
The flower detection we proposed can be integrated into the mobile device to make it convenient to operate in a smart garden which can be applied in flower arrangement and flower horticulture
Summary
Flower classification and detection has been of considerable interest to the computer vision community which can be applied in AIoT for the smart garden. Many researchers perform flower and fruit classification and detection based on convolutional neural network (CNN) approaches [15,16,17,18], which is a kind of feedforward neural network convolution computation contained and a deep structure. It is one of the representative algorithms commonly used in deep learning. Many researchers have applied them in flower and all kinds of fruit detection including all kinds of fruit like mangoes, almonds, and apples [19,20,21,22]
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