Traffic sign recognition has been done in previous practice, but the main use is SSD, and there are relatively few yolo series models. The main purpose here is to develop and build a traffic sign detection and recognition system based on YOLO. First, look at the renderings:
The data set mainly comes from Internet collection and the integration of public data sets such as GSRSB. If you want to know about the data set, you can check my previous article, so I won’t repeat it here.
It was developed and built using the model of the s series, as follows:
# 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)
]
The training data configuration file is as follows:
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"]
There are 45 categories in total.
By default, the iterative calculation of 100 epochs is set, and the result details are as follows:
【F1 Value Curve】
【Precision Curve】
【PR curve】
【Recall Curve】
The training visualization is as follows:
The batch calculation example is as follows:
The development process based on yolov5-lite is detailed with yolov5 as a whole, and the model files are as follows:
# 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)
]I won't repeat the rest