OAM-mode-detection_HOG-SVM
所属分类：人工智能/神经网络/深度学习
开发工具：matlab
文件大小：77329KB
下载次数：4
上传日期：2021-05-17 08:52:54
上 传 者：
sh-1993
说明： OAM模式检测
(OAM-mode-detection)
文件列表:
Data Augmentation (0, 2021-05-17)
Data Augmentation\Data_Augmentation.py (4565, 2021-05-17)
Data Augmentation\step1.py (748, 2021-05-17)
Data Augmentation\step2.py (1645, 2021-05-17)
Data Augmentation\step3.py (2097, 2021-05-17)
Data Augmentation\step4.py (2137, 2021-05-17)
Data Augmentation\step5.py (3261, 2021-05-17)
Data Augmentation\step6.py (2418, 2021-05-17)
Data Augmentation\step7.py (3460, 2021-05-17)
DataSet (0, 2021-05-17)
DataSet\train.txt (78894, 2021-05-17)
DataSet\train (0, 2021-05-17)
DataSet\train\+0.zip (6756225, 2021-05-17)
DataSet\train\+1.zip (6514832, 2021-05-17)
DataSet\train\+2.zip (6876621, 2021-05-17)
DataSet\train\+3.zip (7346506, 2021-05-17)
DataSet\train\+4.zip (7774956, 2021-05-17)
DataSet\train\-1.zip (6568300, 2021-05-17)
DataSet\train\-2.zip (6932719, 2021-05-17)
DataSet\train\-3.zip (7339522, 2021-05-17)
DataSet\train\-4.zip (7741460, 2021-05-17)
DataSet\val.txt (78894, 2021-05-17)
DataSet\val (0, 2021-05-17)
DataSet\val\+0.zip (1677058, 2021-05-17)
DataSet\val\+1.zip (1652258, 2021-05-17)
DataSet\val\+2.zip (1751870, 2021-05-17)
DataSet\val\+3.zip (1829891, 2021-05-17)
DataSet\val\+4.zip (1912937, 2021-05-17)
DataSet\val\-1.zip (1596635, 2021-05-17)
DataSet\val\-2.zip (1699309, 2021-05-17)
DataSet\val\-3.zip (1830043, 2021-05-17)
DataSet\val\-4.zip (1947421, 2021-05-17)
HOG+SVM (0, 2021-05-17)
HOG+SVM\Predict.m (761, 2021-05-17)
HOG+SVM\classifierOfSVM.m (1098, 2021-05-17)
HOG+SVM\extractFeature.m (2895, 2021-05-17)
HOG+SVM\modelpredict.m (263, 2021-05-17)
HOG+SVM\trainClassifier.m (1087070, 2021-05-17)
OAM-Mode-Detection1.png (195572, 2021-05-17)
... ...
# OAM-mode-detection_HOG-SVM
OAM-mode-detection
In the case of OAM transceiver alignment, HOG + SVM is used for mode detection. The functions are as follows:
Python language:
1. Judge whether the folder exists, if not, create a new one, read and save JPG files in batch.
2. Data enhancement (rotation, contrast, brightness, chroma, sharpness), just modify the file path, solve the problem of background missing caused by rotation, select white fill. python
MATLAB
1. Batch reading of images enhanced by Python data;
2. Set different cellsizes to extract hog features;
3. SVM realizes modal detection and classification.
result:
Recognition accuracy is 100%
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