本文采用PaddleOCR开源项目实现渔船牌照识别。因开源数据集少，自行按规则生成1000张渔牌数据，按8:2划分训练集与测试集。经环境安装、预训练模型获取、数据集处理、模型训练等步骤，最终实现识别，虽因训练时长可能效果欠佳，但完成了基本流程。

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基于PaddleOCR渔船牌照识别

一、项目介绍

本文采用PaddleOCR开源项目进行渔船牌照识别，流程分为数据集构建、数据集处理、模型搭建与预测、推理等，由于开源渔船牌照数据集较少，本项目自行构建脚本生成1000多张渔船牌照图进行训练，最终实现渔船牌照识别。        

二、安装环境

In [ ]

# !git clone https://gitee.com/paddlepaddle/PaddleOCR  %cd PaddleOCR
!git checkout -b release/2.4 remotes/origin/release/2.4

    In [ ]

!pwd
!pip install -r requirements.txt
!pip install pillow --user
!pip uninstall  opencv-python -y --user
!pip uninstall opencv-contrib-python -y --user
!pip install opencv-python==4.2.0.32 --user
!pip install --upgrade pip

   

获取预训练模型

选用PaddleOCR模型地址

       

In [ ]

# 获取预训练模型!wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar 
!tar -xf /home/aistudio/PaddleOCR/pretrain_models/en_number_mobile_v2.0_rec_slim_train.tar -C /home/aistudio/PaddleOCR/pretrain_models

   

数据集介绍

由于开源渔牌牌照数据集较少，因此本文选择按照渔牌规则自己生成渔船牌照数据，本次生成1000张渔牌数据，按8:2划分训练集与测试集。生成样例如下：

生成数据脚本参考：https://gitee.com/goalaaa/chinese_license_plate_generator

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三、数据集处理

1. 解压数据集
2. 数据集拆分
3. 格式转换

In [ ]

# 解压数据集!unzip /home/aistudio/data/data201866/fish_dataset.zip -d /home/aistudio/data/fish_data

    In [ ]

# 训练/测试数据清洗path2 = '/home/aistudio/data/fish_data'
 # 数据准备# 格式示例: 1016_752_1.jpg I'm Li Hua,chairman of the Student Union from  with open(f'/home/aistudio/data/label.txt') as f:
    lines = f.readlines()    # 9000用于训练, 1000用于测试
    with open(f'/home/aistudio/data/train.txt', 'w') as f1:        with open(f'/home/aistudio/data/test.txt', 'w') as f2:            for index, line in enumerate(lines): 
                firstSpaceIndex = line.find(' ')
                line2 = line[0:firstSpaceIndex] + '\t' + line[firstSpaceIndex+1:] 
                if index < 800:
                    f1.write(line2)                if index >= 800:
                    f2.write(line2)print("数据处理完成")

   

格式转换

生成用于识别的txt格式

云云湘渔65699.jpg 云云湘渔65699云云葫渔36057.jpg 云云葫渔36057云吉桂渔12572.jpg 云吉桂渔12572云宁云渔83850.jpg 云宁云渔83850云川嘉渔30711.jpg 云川嘉渔30711云川闽渔47501.jpg 云川闽渔47501云川黑渔84624.jpg 云川黑渔84624云新津渔90182.jpg 云新津渔90182云新浙渔03236.jpg 云新浙渔03236云晋豫渔69022.jpg 云晋豫渔69022云桂桂渔07075.jpg 云桂桂渔07075云沪渝渔09603.jpg 云沪渝渔09603云沪渝渔31067.jpg 云沪渝渔31067云浙津渔57087.jpg 云浙津渔57087云渝渝渔29063.jpg 云渝渝渔29063云湘鄂渔35418.jpg 云湘鄂渔35418云烟闽渔62305.jpg 云烟闽渔62305云甘云渔45805.jpg 云甘云渔45805

   

四、模型训练

In [ ]

# 开始训练%cd /home/aistudio/PaddleOCR
!python tools/train.py -c /home/aistudio/work/rec_en_number_lite_train.yml# 等待训练是不是很无聊?让它先跑着,看看下一步吧 :)

    In [ ]

# 开始训练%cd /home/aistudio/PaddleOCR
!python tools/train.py -c /home/aistudio/work/rec_en_number_lite_train_new.yml

   

查看训练过程

1. 在aistudio中打开vdl

       

2. 点击下面的 [启动VisualDL服务]按钮

       

3. 等待vdl服务成功启动后你会看到访问按钮,并点击

       

4. 完成

       

继续训练

1. 我们在训练过程中经常会遇到各种问题导致训练中断,这个时候如果不想从0开始,就需要继续训练了
2. 继续训练的本质是每训练一段时间,就保存一次权重,这样就可以加载最后一次(或者最好)的权重进行训练了

In [ ]

# 这时,上面应该跑了几个epoch了吧,你现在可以把上面的训练停了# 如果上面训练中断了,并且不想再重新开始训练,可以执行本段代码继续上次训练!python tools/train.py -c /home/aistudio/work/rec_en_number_lite_train_new.yml -o Global.checkpoints=/home/aistudio/PaddleOCR/output/rec_en_number_lite_new/latest

    In [10]

# 图片显示import matplotlib.pyplot  as pltimport cv2def imshow(img_path):
    im = cv2.imread(img_path)
    plt.imshow(im )# 随便显示一张图片path2 = '/home/aistudio/data/fish_data/fish_dataset/川辽冀渔96794.jpg'imshow(path2)

       

<Figure size 640x480 with 1 Axes>

                In [20]

# 预测,这里使用当前的训练结果来预测# PS: 由于训练时长问题,效果可能不理想%cd PaddleOCR
!python tools/infer_rec.py -c /home/aistudio/work/rec_en_number_lite_train_new.yml \
       -o Global.infer_img="/home/aistudio/data/fish_data/fish_dataset/川辽冀渔96794.jpg" \
       Global.pretrained_model="/home/aistudio/PaddleOCR/output/rec_en_number_lite_new/latest"# 显示该图片path2 = '/home/aistudio/data/fish_data//fish_dataset/川辽冀渔96794.jpg'imshow(path2)

       

[Errno 2] No such file or directory: 'PaddleOCR'
/home/aistudio/PaddleOCR
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any beh*ior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  def convert_to_list(value, n, name, dtype=np.int):
[2025/03/24 15:00:11] root INFO: Architecture : 
[2025/03/24 15:00:11] root INFO:     Backbone : 
[2025/03/24 15:00:11] root INFO:         model_name : small
[2025/03/24 15:00:11] root INFO:         name : MobileNetV3
[2025/03/24 15:00:11] root INFO:         scale : 0.5
[2025/03/24 15:00:11] root INFO:         small_stride : [1, 2, 2, 2]
[2025/03/24 15:00:11] root INFO:     Head : 
[2025/03/24 15:00:11] root INFO:         fc_decay : 1e-05
[2025/03/24 15:00:11] root INFO:         name : CTCHead
[2025/03/24 15:00:11] root INFO:     Neck : 
[2025/03/24 15:00:11] root INFO:         encoder_type : rnn
[2025/03/24 15:00:11] root INFO:         hidden_size : 48
[2025/03/24 15:00:11] root INFO:         name : SequenceEncoder
[2025/03/24 15:00:11] root INFO:     Transform : None
[2025/03/24 15:00:11] root INFO:     algorithm : CRNN
[2025/03/24 15:00:11] root INFO:     model_type : rec
[2025/03/24 15:00:11] root INFO: Eval : 
[2025/03/24 15:00:11] root INFO:     dataset : 
[2025/03/24 15:00:11] root INFO:         data_dir : /home/aistudio/data/fish_data
[2025/03/24 15:00:11] root INFO:         label_file_list : ['/home/aistudio/data/test.txt']
[2025/03/24 15:00:11] root INFO:         name : SimpleDataSet
[2025/03/24 15:00:11] root INFO:         transforms : 
[2025/03/24 15:00:11] root INFO:             DecodeImage : 
[2025/03/24 15:00:11] root INFO:                 channel_first : False
[2025/03/24 15:00:11] root INFO:                 img_mode : BGR
[2025/03/24 15:00:11] root INFO:             CTCLabelEncode : None
[2025/03/24 15:00:11] root INFO:             RecResizeImg : 
[2025/03/24 15:00:11] root INFO:                 image_shape : [3, 32, 320]
[2025/03/24 15:00:11] root INFO:             KeepKeys : 
[2025/03/24 15:00:11] root INFO:                 keep_keys : ['image', 'label', 'length']
[2025/03/24 15:00:11] root INFO:     loader : 
[2025/03/24 15:00:11] root INFO:         batch_size_per_card : 8
[2025/03/24 15:00:11] root INFO:         drop_last : False
[2025/03/24 15:00:11] root INFO:         num_workers : 8
[2025/03/24 15:00:11] root INFO:         shuffle : False
[2025/03/24 15:00:11] root INFO: Global : 
[2025/03/24 15:00:11] root INFO:     cal_metric_during_train : True
[2025/03/24 15:00:11] root INFO:     character_dict_path : ppocr/utils/EN_symbol_dict.txt
[2025/03/24 15:00:11] root INFO:     checkpoints : None
[2025/03/24 15:00:11] root INFO:     debug : False
[2025/03/24 15:00:11] root INFO:     distributed : False
[2025/03/24 15:00:11] root INFO:     epoch_num : 200
[2025/03/24 15:00:11] root INFO:     eval_batch_step : [0, 100]
[2025/03/24 15:00:11] root INFO:     infer_img : /home/aistudio/data/fish_data/fish_dataset/川辽冀渔96794.jpg
[2025/03/24 15:00:11] root INFO:     infer_mode : False
[2025/03/24 15:00:11] root INFO:     log_smooth_window : 20
[2025/03/24 15:00:11] root INFO:     max_text_length : 25
[2025/03/24 15:00:11] root INFO:     pretrained_model : /home/aistudio/PaddleOCR/output/rec_en_number_lite_new/latest
[2025/03/24 15:00:11] root INFO:     print_batch_step : 10
[2025/03/24 15:00:11] root INFO:     s*e_epoch_step : 3
[2025/03/24 15:00:11] root INFO:     s*e_inference_dir : None
[2025/03/24 15:00:11] root INFO:     s*e_model_dir : ./output/rec_en_number_lite_new
[2025/03/24 15:00:11] root INFO:     use_gpu : True
[2025/03/24 15:00:11] root INFO:     use_space_char : True
[2025/03/24 15:00:11] root INFO:     use_visualdl : True
[2025/03/24 15:00:11] root INFO: Loss : 
[2025/03/24 15:00:11] root INFO:     name : CTCLoss
[2025/03/24 15:00:11] root INFO: Metric : 
[2025/03/24 15:00:11] root INFO:     main_indicator : acc
[2025/03/24 15:00:11] root INFO:     name : RecMetric
[2025/03/24 15:00:11] root INFO: Optimizer : 
[2025/03/24 15:00:11] root INFO:     beta1 : 0.9
[2025/03/24 15:00:11] root INFO:     beta2 : 0.999
[2025/03/24 15:00:11] root INFO:     lr : 
[2025/03/24 15:00:11] root INFO:         learning_rate : 0.005
[2025/03/24 15:00:11] root INFO:         name : Cosine
[2025/03/24 15:00:11] root INFO:     name : Adam
[2025/03/24 15:00:11] root INFO:     regularizer : 
[2025/03/24 15:00:11] root INFO:         factor : 1e-05
[2025/03/24 15:00:11] root INFO:         name : L2
[2025/03/24 15:00:11] root INFO: PostProcess : 
[2025/03/24 15:00:11] root INFO:     name : CTCLabelDecode
[2025/03/24 15:00:11] root INFO: Train : 
[2025/03/24 15:00:11] root INFO:     dataset : 
[2025/03/24 15:00:11] root INFO:         data_dir : /home/aistudio/data/fish_data
[2025/03/24 15:00:11] root INFO:         label_file_list : ['/home/aistudio/data/train.txt']
[2025/03/24 15:00:11] root INFO:         name : SimpleDataSet
[2025/03/24 15:00:11] root INFO:         transforms : 
[2025/03/24 15:00:11] root INFO:             DecodeImage : 
[2025/03/24 15:00:11] root INFO:                 channel_first : False
[2025/03/24 15:00:11] root INFO:                 img_mode : BGR
[2025/03/24 15:00:11] root INFO:             RecAug : None
[2025/03/24 15:00:11] root INFO:             CTCLabelEncode : None
[2025/03/24 15:00:11] root INFO:             RecResizeImg : 
[2025/03/24 15:00:11] root INFO:                 image_shape : [3, 32, 320]
[2025/03/24 15:00:11] root INFO:             KeepKeys : 
[2025/03/24 15:00:11] root INFO:                 keep_keys : ['image', 'label', 'length']
[2025/03/24 15:00:11] root INFO:     loader : 
[2025/03/24 15:00:11] root INFO:         batch_size_per_card : 64
[2025/03/24 15:00:11] root INFO:         drop_last : True
[2025/03/24 15:00:11] root INFO:         num_workers : 4
[2025/03/24 15:00:11] root INFO:         shuffle : True
[2025/03/24 15:00:11] root INFO: profiler_options : None
[2025/03/24 15:00:11] root INFO: train with paddle 2.0.2 and device CUDAPlace(0)
W0324 15:00:11.964440  6307 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0324 15:00:11.970218  6307 device_context.cc:372] device: 0, cuDNN Version: 7.6.
[2025/03/24 15:00:15] root INFO: load pretrain successful from /home/aistudio/PaddleOCR/output/rec_en_number_lite_new/latest
[2025/03/24 15:00:15] root INFO: infer_img: /home/aistudio/data/fish_data/fish_dataset/川辽冀渔96794.jpg
[2025/03/24 15:00:15] root INFO: 	 result: 川辽冀96794	0.93036735
[2025/03/24 15:00:15] root INFO: success!

       

<Figure size 640x480 with 1 Axes>

               

以上就是基于PaddleOCR的渔船牌照识别的详细内容，更多请关注其它相关文章！

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