基于根茬检测的秋后残膜回收导航路径提取方法

doi:10.13229/j.cnki.jdxbgxb20200500

Abstract

Abstract:

In view of the path planning problem in the visual navigation of the residual film recovery operation after the autumn, a method of path extraction for residual film recovery operation is proposed. The color space and texture features of the three types of images of stubble, residual film and inter-row noises were extracted to constitute a training set and test set with ratio of 8:2. With the goal of detected root, a Random Forest (RF) model is built to classify the sample images. The feature dimensions are reduced by the feature importance and correlation, the optimal model parameters of the RF model are determined by grid search. Based on the upright characteristics of the stubble, the upper and lower vertices of each stubble are detected as the feature points. All the feature points are fitted by the least square method as the navigation line. The experimental results show that the 30 feature dimensions are reduced to 16 feature dimensions by feature selection, and the processing time of a single image is reduced from 0.24 seconds to 0.16 seconds. Under the optimal parameters of the model, the accuracy of the test set is 92.5%. The classification test was selected for 450 images of 10× 20 pixels, the accuracy rate is 91.8%, the accuracy rate (sensitivity rate) of stubble detection is 90.7%. 200 field operation images with ROI area size of 100×200 pixels were selected for the navigation line fitting experiment, which included different weather, different depression angles and abnormal driving images, 171 images were successfully fitted. The stubble detection method has high stability and accuracy, and can provide reference for root crop detection and path extraction of different crops.

Key words: machine vision, residual film recycling, navigation path, feature selection, random forest, least square method

CLC Number:

Jing-bin LI,Yu-kun YANG,Bao-qin WEN,Za KAN,Wen SUN,Shuo YANG. Method of extraction of navigation path of post-autumn residual film recovery based on stubble detection[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1528-1539.

Figures/Tables 20

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Color feature calculation formula"

特征	计算公式
R分量平均值	$R m = ∑ j = 1 20 ∑ i = 1 10 R (i, j) 10 × 20$
G分量平均值	$G m = ∑ j = 1 20 ∑ i = 1 10 G (i, j) 10 × 20$
B分量平均值	$B m = ∑ j = 1 20 ∑ i = 1 10 B (i, j) 10 × 20$
H分量平均值	$H m = ∑ j = 1 20 ∑ i = 1 10 H (i, j) 10 × 20$
S分量平均值	$S m = ∑ j = 1 20 ∑ i = 1 10 S (i, j) 10 × 20$
V分量平均值	$V m = ∑ j = 1 20 ∑ i = 1 10 V (i, j) 10 × 20$

Table 1

Fig.5

Table 2

Gray-level co-occurrence matrix feature calculation formula"

特征	计算公式
二阶矩（asm）	$a s m = ∑ i = 1 k ∑ j = 1 k (G (i, j)) 2$
熵（ent）	$e n t = - ∑ i = 1 k ∑ j = 1 k G (i, j) l o g G (i, j)$
相关性（cor）	$c o r = ∑ i = 1 k ∑ j = 1 k (i j) G (i, j) - u i u j s i s j$
差异性（dis）	$d i s = ∑ i = 1 k ∑ j = 1 k G (i, j) \| i - j \|$
对比度（con）	$c o n = ∑ n = 0 k - 1 n 2 ∑ i = 1 k ∑ j = 1 k G (i, j), \| i - j \| = n$
逆差矩（hom）	$h o m = ∑ i = 1 k ∑ j = 1 k G (i, j) 1 + (i - j) 2$

Table 2

Fig.6

Fig.7

Table 3

Fig.8

Fig.9

Table 4

Table 5

Fig.10

Table 6

Evaluation of classification effects"

指标	公式	数值
准确率/%	$A c = T P + T N T P + T N + F P + F N$	91.8
敏感性/%	$S e = T P T P + F N$	90.7
特异性/%	$S p = T N T N + F P$	92.3

Table 6

Table 7

Fig.11

Fig.12

Fig.13

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分类特征	准确性/%	增长幅度/%
GLCM（0）	84.0
GLCM（0）+ GLCM（90）	90.1	6.1
GLCM（0）+ GLCM（90）+ GLCM（45）	91.5	1.4
GLCM（0）+ GLCM（90）+ GLCM（45）+GLCM（135）	92.5	1.0
GLCM（0）+ GLCM（90）+ GLCM（45）+GLCM（135）+HSV	93.1	0.6
GLCM（0）+ GLCM（90）+ GLCM（45）+GLCM（135）+HSV+RGB	93.3	0.2

随机森林参数	数值	随机森林参数	数值
n_estimators	50	min_samples_split	2
max_depth	11	max_features	4
min_samples_leaf	1

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Method of extraction of navigation path of post-autumn residual film recovery based on stubble detection

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类别	根茬	行间茎秆碎叶	残膜
根茬	136	10	4
行间茎秆碎叶	15	128	7
残膜	8	3	139