使用yolov5-6.0源码、yolov5x.yaml、yolov5x.pt
1、在主干网络中, 加入CBAM 注意力模块增强网络特征提取能力
参考:加入CBAM
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, CABlock, [128, 4]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, 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, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
2、在颈部网络部分, 使用 BiFPN 结构替换 PANet 结构, 强化底层特征利用; (将所有的Concat改为BiFPN_Concat2)
参考: BiFPN
# YOLOv5 head
head:
[[-1, 1, Conv, [512, 1, 1]], #20*20
[-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
[[-1, 6], 1, BiFPN_Concat2, [1]], # cat backbone P4 40*40
[-1, 3, BottleneckCSP, [512, False]], # 13 40*40
[-1, 1, Conv, [512, 1, 1]], #40*40
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, BiFPN_Concat2, [1]], # cat backbone P3 80*80
[-1, 3, BottleneckCSP, [512, False]], # 17 (P3/8-small) 80*80
[-1, 1, Conv, [256, 1, 1]], #18 80*80
[-1, 1, nn.Upsample, [None, 2, 'nearest']], #19 160*160
[[-1, 2], 1, BiFPN_Concat2, [1]], #20 cat backbone p2 160*160
[-1, 3, BottleneckCSP, [256, False]], #21 160*160
[-1, 1, Conv, [256, 3, 2]], #22 80*80
[[-1, 18], 1, BiFPN_Concat2, [1]], #23 80*80
[-1, 3, BottleneckCSP, [256, False]], #24 80*80
[-1, 1, Conv, [256, 3, 2]], #25 40*40
[[-1, 14], 1, BiFPN_Concat2, [1]], # 26 cat head P4 40*40
[-1, 3, BottleneckCSP, [512, False]], # 27 (P4/16-medium) 40*40
[-1, 1, Conv, [512, 3, 2]], #28 20*20
[[-1, 10], 1, BiFPN_Concat2, [1]], #29 cat head P5 #20*20
[-1, 3, BottleneckCSP, [1024, False]], # 30 (P5/32-large) 20*20
[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(p2, P3, P4, P5)
]
3、在检测头部分, 增加高分辨率检测头, 改善对于微小目标的检测能力
参考:检测头
# Parameters
nc: 2 # number of classes
depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # layer channel multiple
anchors:
- [5,6, 8,14, 15,11] #4
- [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 head
head:
[[-1, 1, Conv, [512, 1, 1]], #20*20
[-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
[[-1, 6], 1, BiFPN_Concat2, [1]], # cat backbone P4 40*40
[-1, 3, BottleneckCSP, [512, False]], # 13 40*40
[-1, 1, Conv, [512, 1, 1]], #40*40
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, BiFPN_Concat2, [1]], # cat backbone P3 80*80
[-1, 3, BottleneckCSP, [512, False]], # 17 (P3/8-small) 80*80
[-1, 1, Conv, [256, 1, 1]], #18 80*80
[-1, 1, nn.Upsample, [None, 2, 'nearest']], #19 160*160
[[-1, 2], 1, BiFPN_Concat2, [1]], #20 cat backbone p2 160*160
[-1, 3, BottleneckCSP, [256, False]], #21 160*160
[-1, 1, Conv, [256, 3, 2]], #22 80*80
[[-1, 18], 1, BiFPN_Concat2, [1]], #23 80*80
[-1, 3, BottleneckCSP, [256, False]], #24 80*80
[-1, 1, Conv, [256, 3, 2]], #25 40*40
[[-1, 14], 1, BiFPN_Concat2, [1]], # 26 cat head P4 40*40
[-1, 3, BottleneckCSP, [512, False]], # 27 (P4/16-medium) 40*40
[-1, 1, Conv, [512, 3, 2]], #28 20*20
[[-1, 10], 1, BiFPN_Concat2, [1]], #29 cat head P5 #20*20
[-1, 3, BottleneckCSP, [1024, False]], # 30 (P5/32-large) 20*20
[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(p2, P3, P4, P5)
]