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工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

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基于FPN_Tensorflow的PCB缺陷检测

这一篇实战，参考于:
基于深度学习的印刷电路板瑕疵识别.

因为介绍的数据集的预处理不是很详细，所以我在这里再整理一下，顺便学习一下新的深度学习网络模型。

[En]

Because the preprocessing of the data set introduced is not very detailed, so I will sort it out again here and learn the new deep learning network model by the way.

一. 原理
Faster R-CNN.

深度学习网络 | FPN和fast rcnn及RPN网络是如何结合的细节.

二. 训练过程
1.数据准备
你可以从这里下载数据集：http://robotics.pkusz.edu.cn/resources/dataset/
我已经根据从官方网站上的数据集下载的数据分配了训练数据和验证数据。

[En]

Mine has allocated the training data and verification data, according to the data downloaded from the dataset on the official website.

下载原始数据集后，你会发现里面有这么多文件夹：

[En]

After downloading the original dataset, you will find that there are so many folders:

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测
Annotations文件夹：
工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

需要把各个文件夹的文件全部移动到 Annotations/，想这样：

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

同样，对 images/，就这样：

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

6个文件夹的图片全部移动出来，然后把空的文件夹删掉。

2.下载源代码

环境要求：注意版本匹配

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

3.数据拆分
将您的数据集划分为训练集和测试集：

[En]

Split your data set into training sets and test sets:

python /data/io/divide_data.py

将会生成两个文件夹：

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

接着把数据集文件夹放到data文件夹里面：

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

4.修改配置

在/libs/label_name_dict/label_dict.py中添加自己的数据集标签类别。具体如下：

-*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import

from libs.configs import cfgs

if cfgs.DATASET_NAME == 'ship':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'ship': 1
    }
elif cfgs.DATASET_NAME == 'aeroplane':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'aeroplane': 1
    }
elif cfgs.DATASET_NAME == 'WIDER':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'face': 1
    }
elif cfgs.DATASET_NAME == 'jyzdata':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'classone': 1,
        'boli': 2,
        'dangeboli': 3,
        'taoci': 4,
        'taoci2': 4
    }
elif cfgs.DATASET_NAME == 'icdar':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'text': 1
    }
elif cfgs.DATASET_NAME.startswith('DOTA'):
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'roundabout': 1,
        'tennis-court': 2,
        'swimming-pool': 3,
        'storage-tank': 4,
        'soccer-ball-field': 5,
        'small-vehicle': 6,
        'ship': 7,
        'plane': 8,
        'large-vehicle': 9,
        'helicopter': 10,
        'harbor': 11,
        'ground-track-field': 12,
        'bridge': 13,
        'basketball-court': 14,
        'baseball-diamond': 15
    }
elif cfgs.DATASET_NAME == 'pascal':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'aeroplane': 1,
        'bicycle': 2,
        'bird': 3,
        'boat': 4,
        'bottle': 5,
        'bus': 6,
        'car': 7,
        'cat': 8,
        'chair': 9,
        'cow': 10,
        'diningtable': 11,
        'dog': 12,
        'horse': 13,
        'motorbike': 14,
        'person': 15,
        'pottedplant': 16,
        'sheep': 17,
        'sofa': 18,
        'train': 19,
        'tvmonitor': 20
    }
elif cfgs.DATASET_NAME == 'pcb':
    NAME_LABEL_MAP = {
        'back_ground': 0,
        'missing_hole': 1,
        'mouse_bite': 2,
        'open_circuit': 3,
        'short': 4,
        'spur': 5,
        'spurious_copper': 6
    }
else:
    assert 'please set label dict!'

def get_label_name_map():
    reverse_dict = {}
    for name, label in NAME_LABEL_MAP.items():
        reverse_dict[label] = name
    return reverse_dict

LABEl_NAME_MAP = get_label_name_map()

然后就可以运行convert_data_to_tfrecord.py了。别忘了填上四个参数。

python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'

5.数据转换
下一步，需要分别将训练和测试数据转换成 TFrecord 格式。该格式是TensorFlow的高效存储格式，连续的内存二进制存储能够有效的加快数据的读取和写入。而像原始文件夹那样每个数据独立存储。

python convert_data_to_tfrecord.py --VOC_dir='***/data/pcb_test/' --save_name='test' --img_format='.jpg' --dataset='pcb'

python convert_data_to_tfrecord.py --VOC_dir='***/data/pcb_train/' --save_name='train' --img_format='.jpg' --dataset='pcb'

上一步训练和测试数据分离后的文件夹路径<details><summary>*<font color='gray'>[En]</font>*</summary>*<font color='gray'>Folder path after separation of training and test data in the previous step</font>*</details>
--VOC_dir='你的数据路径'

#tfrecord名字。训练数据用'train' ,测试数据用'test'
--save_name='train'

#你的图片格式。有Png\tif\jpeg
--img_format='.jpg'

#你的数据集名字。这个需要在/libs/label_name_dict/label_dict.py中添加自己的数据集
--dataset='mydataset

世代后的外观和位置如下：

[En]

The appearance and location after generation are as follows:

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测
该文件大小很大，几个G。
6.参数设置

对应里面的model，应先下载好预训练模型，放在 /data/pretrained_weights/ 中。

预训练模型的下载地址：
Res101: resnet_v1_101_2016_08_28.tar.gz.

Res50: resnet_v1_50_2016_08_28.tar.gz.

数据准备好了，那么训练就要调参和调一些路径啦。打开/libs/configs/cfgs.py。

-*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import os
import tensorflow as tf
------------------------------------------------
VERSION = 'FPN_Res101_0117_OHEM'#-----------------改
NET_NAME = 'resnet_v1_101'#-----------------改
ADD_BOX_IN_TENSORBOARD = True

---------------------------------------- System_config
ROOT_PATH = os.path.abspath('/Xia-Detection-for-PCB的绝对路径')#工程文件绝对路径-----------------改
print (20*"++--")
print (ROOT_PATH)
GPU_GROUP = "2"
SHOW_TRAIN_INFO_INTE = 10
SMRY_ITER = 100
SAVE_WEIGHTS_INTE = 10000

SUMMARY_PATH = ROOT_PATH + '/output/summary'
TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result'
INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image'
INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results'

if NET_NAME.startswith("resnet"):
    weights_name = NET_NAME
elif NET_NAME.startswith("MobilenetV2"):
    weights_name = "mobilenet/mobilenet_v2_1.0_224"
else:
    raise NotImplementedError

PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt'
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')

EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/'
test_annotate_path = '/pcb数据集/PCB_DATASET/Annotations/'#绝对路径-----------------改

------------------------------------------ Train config
RESTORE_FROM_RPN = False
IS_FILTER_OUTSIDE_BOXES = False
FIXED_BLOCKS = 0  # allow 0~3
USE_07_METRIC = False

RPN_LOCATION_LOSS_WEIGHT = 1.

RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0

FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0
FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0
RPN_SIGMA = 3.0
FASTRCNN_SIGMA = 1.0

MUTILPY_BIAS_GRADIENT = None   # 2.0  # if None, will not multipy
GRADIENT_CLIPPING_BY_NORM = None   # 10.0  if None, will not clip

EPSILON = 1e-5
MOMENTUM = 0.9
LR = 0.001  # 0.001  # 0.0003
#DECAY_STEP = [60000, 80000]  # 50000, 70000
DECAY_STEP = [10000, 20000]  # 50000, 70000
#MAX_ITERATION = 150000
MAX_ITERATION = 30000

------------------------------------------- Data_preprocess_config
DATASET_NAME = 'pcb'  # 'ship', 'spacenet', 'pascal', 'coco' #-----------------改
PIXEL_MEAN = [123.68, 116.779, 103.939]  # R, G, B. In tf, channel is RGB. In openCV, channel is BGR
PIXEL_MEAN = [21.25, 85.936, 28.729]  #-----------------改
IMG_SHORT_SIDE_LEN =  600  # 600
IMG_MAX_LENGTH =  3000  # 1000
CLASS_NUM = 6 #-----------------改

--------------------------------------------- Network_config
BATCH_SIZE = 1
INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01)
BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001)
WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001

---------------------------------------------Anchor config
USE_CENTER_OFFSET = False

LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6']
BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512]  # addjust the base anchor size for voc.

BASE_ANCHOR_SIZE_LIST = [15, 25, 40, 60, 80]  # addjust the base anchor size for voc.

#BASE_ANCHOR_SIZE_LIST = [8, 15, 25, 40, 60]
ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64]
ANCHOR_SCALES = [2., 3., 4.]
ANCHOR_RATIOS = [2., 3., 4., 5.]
ANCHOR_SCALES = [1.0]
ANCHOR_RATIOS = [0.5, 1., 2.0]
ROI_SCALE_FACTORS = [10., 10., 5.0, 5.0]
ANCHOR_SCALE_FACTORS = None

--------------------------------------------FPN config
SHARE_HEADS = True
KERNEL_SIZE = 3
RPN_IOU_POSITIVE_THRESHOLD = 0.7
RPN_IOU_NEGATIVE_THRESHOLD = 0.3
TRAIN_RPN_CLOOBER_POSITIVES = False

RPN_MINIBATCH_SIZE = 256
RPN_POSITIVE_RATE = 0.5
RPN_NMS_IOU_THRESHOLD = 0.7
RPN_TOP_K_NMS_TRAIN = 12000
#RPN_MAXIMUM_PROPOSAL_TARIN = 2000
RPN_MAXIMUM_PROPOSAL_TARIN = 2000

RPN_TOP_K_NMS_TEST = 6000
RPN_MAXIMUM_PROPOSAL_TEST = 1000

specific settings for FPN
FPN_TOP_K_PER_LEVEL_TRAIN = 2000
FPN_TOP_K_PER_LEVEL_TEST = 1000

-------------------------------------------Fast-RCNN config
ROI_SIZE = 14
ROI_POOL_KERNEL_SIZE = 2
#USE_DROPOUT = False
USE_DROPOUT = True
KEEP_PROB = 1.0
SHOW_SCORE_THRSHOLD = 0.6  # only show in tensorboard

#FAST_RCNN_NMS_IOU_THRESHOLD = 0.3  # 0.6
FAST_RCNN_NMS_IOU_THRESHOLD = 0.3
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100
FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5
FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0   # 0.1 < IOU < 0.5 is negative
FAST_RCNN_MINIBATCH_SIZE = 256  # if is -1, that is train with OHEM
FAST_RCNN_MINIBATCH_SIZE = -1
FAST_RCNN_POSITIVE_RATE = 0.25

#ADD_GTBOXES_TO_TRAIN = False
ADD_GTBOXES_TO_TRAIN = True

注意路径和参数的配置。

[En]

Pay attention to the configuration of paths and parameters.

7.训练

python /tools/train.py

等着训练完成吧。

8.验证测试

cd $PATH_ROOT/tools
python inference.py --data_dir='/PATH/TO/IMAGES/'
                    --save_dir='/PATH/TO/SAVE/RESULTS/'
                    --GPU='0'

训练完之后会在output/summary文件夹里，生成

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

9.效果

研究发现，效根训练的迭代次数与迭代次数有很大关系，训练时间越长，训练的前两天，迭代值为30000。

[En]

It is found that the number of iterations of effect root training has a lot to do with the number of iterations, the longer the training time, the first two days of training, the iterative value of 30000.

工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

需要源代码包的私信我就可啦。期待下一次的实战项目。

Original: https://blog.csdn.net/weixin_39735688/article/details/121502493
Author: 夏融化了这季节
Title: 工业缺陷检测项目实战(三)——基于FPN_Tensorflow的PCB缺陷检测

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