c++ yolov5-6.0网络添加小目标检测头 TensorRT部署

小目标检测使用TPH-YOLOv5 中的yolov5l-xs-tph.yaml模型；对其中的C3STR替换为C3模块；本文的TensorRT部署是基于yolov5l-xs-tph修改后的模型训练部署，其精度一般主要学习如何TensorRT部署，是在wang-xinyu基础上简单修改；

TPH-YOLOv5 ：https://github.com/cv516Buaa/tph-yolov5 yolov5l-xs-tph.yaml模型修改后：tensorrt-nyy: ​yolov5-6.0网络添加小目标检测头 TensorRT部署；​

1、模型训练 具体修改在head 部分 anchors改为4； C3STR改为C3

# YOLOv5  by Ultralytics, GPL-3.0 license

# Parameters

nc: 10 # number of classes

depth_multiple: 1.0 # model depth multiple

width_multiple: 1.0 # layer channel multiple

anchors: 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

# 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 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, 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

]

# YOLOv5 v6.0 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, [ 128, 1, 1 ] ],

[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],

[ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2

[ -1, 2, C3, [ 128, False ] ], # 21 (P2/4-xsmall) C3STR

[ -1, 1, Conv, [ 128, 3, 2 ] ],

[ [ -1, 18, 4], 1, Concat, [ 1 ] ], # cat head P3

[ -1, 2, C3, [ 256, False ] ], # 24 (P3/8-small) C3STR

[-1, 1, Conv, [256, 3, 2]],

[[-1, 14, 6], 1, Concat, [1]], # cat head P4

[-1, 2, C3, [512, False]], # 27 (P4/16-medium) C3STR

[-1, 1, Conv, [512, 3, 2]],

[[-1, 10], 1, Concat, [1]], # cat head P5

[-1, 2, C3, [1024, False]], # 30 (P5/32-large) C3STR

[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)

]

yolov5l-xs-tph.yaml模型修改后训练60epoch后的模型，其精度一般，训练数据使用VisDrone2019，训练结果如下图：

2、模型转换部署

按照wang-xinyu的步骤转换即可；

主要修改的地方：具体细节参考：tensorrt-nyy: ​yolov5-6.0网络添加小目标检测头 TensorRT部署；​ CHECK_COUNT=3 修改为 CHECK_COUNT=4； MAX_OUTPUT_BBOX_COUNT=1000 修改为MAX_OUTPUT_BBOX_COUNT=4000 TensorRT 层的添加主要在第17层以后的18-31层添加修改； 在scale = 8  修改为 scale = 4； 修改后测试图片如下：

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