﻿ 基于AutoML的保护区物种识别
 计算机系统应用  2019, Vol. 28 Issue (9): 147-153 PDF

1. 中国科学院 计算机网络信息中心, 北京 100190;
2. 中国科学院大学, 北京 100049

Species Recognition of Protected Area Based on AutoML
LIU Yao1,2, LUO Ze1
1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract: With the increase of investment in ecological protection, the application of infrared camera technology in natural reserves has developed rapidly. Species recognition, which is particularly important in how to fully mine photo information, is the premise of other work. In image recognition, with the outbreak of deep learning, the image recognition has been revolutionized. Convolutional neural network as the representative network structure almost completely overcomes the traditional method in accuracy. However, due to the huge impact of the network structure on the accuracy of the final image recognition, people often choose a network structure suitable for their own dataset from some classic network structures, such as VGG16, VGG19, ResNet50, and so on, in practical applications. Nevertheless, it may need to re-select network structure for different datasets. Therefore, in the species recognition of protected area, this study proposes an automatic construction network structure technology based on AutoML. The technology can automatically build appropriate network structures for different datasets of protected area to avoid manual selection of network structures. At the same time, the technology achieves an accuracy comparable to manual selection of network structures.
Key words: species recognition     auto machine learning     auto construct network structures

1 相关理论 1.1 贝叶斯优化

(1)假设输入之间服从高斯分布模型M.

(2)从M中选择下一个采集函数值较高的输入 $x$ .

(3)观察输入 $x$ 的输出 $y$ , 如果 $y$ 满足要求, 那么结束算法; 否则, 返回 $\left( {x,y} \right)$ 修正高斯分布模型M, 返回第(2)步.

1.2 模拟退火

(1) 初始化: 初始温度T (充分大), 初始解状态S (是算法迭代的起点), 每个T值的迭代次数L.

(2) 对 $k = 1,2, \cdots ,L$ 做第(3)至第(6)步.

(3) 产生新解 $S'$ .

(4) 计算增量 $\Delta T = C(S') - C(S)$ , 其中 $C(S)$ 为评价函数.

(5) 若 $\Delta T < 0$ , 则接受 $S'$ 作为新的当前解, 否则以概率 ${e^{\frac{{ - \Delta T}}{T}}}$ 接受 $S'$ 作为新的当前解.

(6) 如果满足终止条件则输出当前解作为最优解, 结束程序.

(7) $T$ 逐渐减少, 且 $T \leftarrow 0$ , 然后转第2步.

2 本文方法

 图 1 AutoML技术

2.1 经典的网络结构组件

2.2 自动构建网络结构

(1)从候选队列头取出网络结构 $G$ .

(2)将 $G$ 进行上述4种扩展操作, 得到4个新的网络结构, 将新的网络添加到候选队列中.

2.3 搜索网络结构

 图 2 自动构建网络结构

 $\alpha \left( f \right) = \mu \left( f \right) + \beta \sigma \left( f \right)$

$\;\beta$ 是一个平衡因子, 来平衡探索与利用. 得到每个网络结构的 $\alpha$ 值之后, 通过模拟退火算法来选取下一个网络结构作为备训练的网络结构.

(1)初始化模拟退火的温度衰减率 $r$ , 温度参数 $T$ 以及最低温度阈值 ${T_{\rm{low}}}$ , 最高历史模型性能值 ${c_{\max }}$ , 最优网络结构 ${f_{\max }}$ , 优先级搜索队列Q.

(2)取出队列头网络结构 $f$ , 对该网络结构做上述四种操作进行扩增, 得到四个新的网络结构, 对于每个新网络结构 $f'$ , 如果 ${e^{\frac{{\alpha \left( {f'} \right) - {c_{\max }}}}{T}}} > Rand()$ , 将该网络加入到优先级搜索队列中, 否则不加入队列. 如果 ${c_{\max }} <$ $\alpha \left( {f'} \right)$ , 那么 ${c_{\max }} \leftarrow \alpha \left( {f'} \right),{f_{\max }} \leftarrow f'$ . 同时衰减温度 $T \leftarrow T \times r$ .

(3)如果队列不为空, 并且 $T > {T_{\rm{low}}}$ , 返回第二步; 否则, 返回最优网络结构 ${f_{\max }}$ .

2.4 算法流程

 图 3 算法流程图

3 实验分析 3.1 数据集

3.2 模型训练

3.3 实验结果

4 结论与展望

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