基于轻量级yolov5/yolov5-lite开发构建交通标识检测识别系统

交通标识识别在之前的实践中做过，但是用的主要是SSD，比较少接触到yolo系列的模型，这里主要就是想基于YOLO开发构建交通标识检测识别系统，首先看下效果图：

数据集的话主要是来源于互联网采集还有GSRSB等公开数据集的整合。想了解数据集相关的可以查看我之前的文章，这里就不再赘述了。

使用的是s系列的模型开发构建的，如下：

# parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple

# anchors
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 9, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 3, C3, [1024, False]],  # 9
  ]

# YOLOv5 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

训练数据配置文件如下：


train: dataset/images/train  
val: dataset/images/val  


# number of classes
nc: 45

# class names
names: ["i2","i4","i5","il100","il60","il80","io","ip","p10","p11","p12","p19","p23","p26","p27","p3","p5","p6","pg",
           "ph4","ph4.5","ph5","pl100","pl120","pl20","pl30","pl40","pl5","pl50","pl60","pl70","pl80","pm20","pm30","pm55",
           "pn","pne","po","pr40","w13","w32","w55","w57","w59","wo"]

一共有45个类别。

默认设定100次epoch的迭代计算，结果详情如下：

【F1值曲线】

【Precision曲线】

【PR曲线】

【Recall曲线】

训练可视化如下：

batch计算实例如下：

基于yolov5-lite的开发流程与yolov5整体详细，模型文件如下：

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
anchors:
  - [ 10,13, 16,30, 33,23 ]  # P3/8
  - [ 30,61, 62,45, 59,119 ]  # P4/16
  - [ 116,90, 156,198, 373,326 ]  # P5/32

# custom backbone
backbone:
  # [from, number, module, args]
  [ [ -1, 1, CBH, [ 32, 3, 2 ] ],    # 0-P2/4
    [ -1, 1, LC_Block, [ 64, 2, 3, False ] ], # 1-P3/8
    [ -1, 1, LC_Block, [ 64, 1, 3, False ] ], # 2-P4/16
    [ -1, 1, LC_Block, [ 128, 2, 3, False ] ], # 3
    [ -1, 1, LC_Block, [ 128, 1, 3, False ] ], # 4-P5/32
    [ -1, 1, LC_Block, [ 128, 1, 3, False ] ], # 5
    [ -1, 1, LC_Block, [ 128, 1, 3, False ] ], # 6
    [ -1, 1, LC_Block, [ 256, 2, 3, False ] ], # 7-P5/32
    [ -1, 1, LC_Block, [ 256, 1, 5, False ] ],
    [ -1, 1, LC_Block, [ 256, 1, 5, False ] ],
    [ -1, 1, LC_Block, [ 256, 1, 5, False ] ], # 10-P5/32
    [ -1, 1, LC_Block, [ 256, 1, 5, False ] ],
    [ -1, 1, LC_Block, [ 256, 1, 5, False ] ], # 12-P5/32
    [ -1, 1, LC_Block, [ 512, 2, 5, True ] ],
    [ -1, 1, LC_Block, [ 512, 1, 5, True ] ], # 14-P5/32
    [ -1, 1, LC_Block, [ 512, 1, 5, True ] ], # 15
    [ -1, 1, LC_Block, [ 512, 1, 5, True ] ], # 16
    [ -1, 1, Dense, [ 512, 1, 0.2 ] ],
  ]

# v5Lite-c head
head:
  [ [-1, 1, Conv, [256, 1, 1]], # 18
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat backbone P4
    [-1, 1, C3, [256, False]],  # 21

    [-1, 1, Conv, [128, 1, 1]], # 22
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
    [-1, 1, C3, [128, False]],  # 25 (P3/8-small)

    [ -1, 1, LC_Block, [ 128, 2, 5, True ] ],  # 26
    [ [ -1, 22 ], 1, Concat, [ 1 ] ],  # cat head P4
    [-1, 1, C3, [256, False]],  # 28 (P4/16-medium)

    [ -1, 1, LC_Block, [ 256, 2, 5, True ] ], # 29
    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P5
    [-1, 1, C3, [512, False]],  # 31 (P5/32-large)

    [ [ 25, 28, 31 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
  ]

其余的就不再赘述了

基于轻量级yolov5/yolov5-lite开发构建交通标识检测识别系统

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