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安全帽识别,安全帽检测yolo可以检测图片,视频流,有界面python识别率99%
代码语言:javascript复制# parameters
nc: 3 # number of classes <============ 修改这里为数据集的分类数
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
# anchors
anchors: # <============ 根据 ./data/gen_anchors/anchors.txt 中的 Best Anchors 修改,需要取整(可选)
- [14,27, 23,46, 28,130]
- [39,148, 52,186, 62.,279]
- [85,237, 88,360, 145,514]
# 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, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [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, BottleneckCSP, [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, BottleneckCSP, [256, False]], # 17
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
效果图:
效果视频:
https://player.bilibili.com/player.html?aid=972775064
python yolo 安全帽识别
项目代码下载:
Python工地安全帽识别安全帽检测预警yolo可以检测图片,视频流,有界面python商用源码-互联网文档类资源-CSDN下载
0基础部署该项目视频教程:
商用Python工地安全帽识别安全帽检测预警yolo可以检测图片,视频流,有界面python商用源码视频讲解-深度学习文档类资源-CSDN下载
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