30-Linux NPU YOLOV5S 目标检测

1. NPU YOLOV5S 是什么

YOLOV5S 是计算机视觉中的一个目标检测算法，它是YOLO（You Only Look Once）系列的一部分，由Joseph Redmon和Alexey Bochkovskiy等人开发。YOLO 算法系列以其快速的实时目标检测能力而闻名，能够在图像或视频中检测和定位多个对象，包括物体的类别和位置。

YOLOv5S 是 YOLOv5 系列中的一个变种，不同版本的 YOLOv5 具有不同的性能和模型复杂度。通常，"S" 在 YOLOv5S 中可能代表 "Small"，这意味着它可能是一种轻量级的模型，适合在资源受限的环境下运行，如移动设备或嵌入式系统。 YOLOv5S 可能在速度和准确性之间进行权衡，以适应不同的应用场景。

2. 解析图片及视频

RK Linux官方提供rknn_yolov5_demo，源码支持 RK3562、RK3566、RK3568、RK3588，编译出工具可进行图像解析和视频解析

源码路径

(SDK)/external/rknpu2/examples/rknn_yolov5_demo

源码参考及编译

配置环境

$ export TOOL_CHAIN=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/
$ export GCC_COMPILER=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

SDK目录为指代实际中要替换成自己的实际目录

编译对应的工具

$ cd external/rknpu2/examples/rknn_yolov5_demo/
$ ./build-linux_RK3562.sh

build-android_RK3562.sh build-android_RK3566_RK3568.sh build-android_RK3588.sh build-linux_RK3562.sh build-linux_RK3566_RK3568.sh build-linux_RK3588.sh
根据实际IC选择对应的脚本进行编译

生成工具路径

rknn_yolov5_demo$ ls install/rknn_yolov5_demo_Linux/
lib  model  rknn_yolov5_demo  rknn_yolov5_video_demo

拷贝到主板上运行使用

工具使用

rknn_yolov5_demo

Usage: ./rknn_yolov5_demo <rknn model> <jpg>

rknn_yolov5_demo 使用示例

$ ./rknn_yolov5_demo model/RK3588/yolov5s-640-640.rknn model/bus.jpg
post process config: box_conf_threshold = 0.25, nms_threshold = 0.45
Read model/bus.jpg ...
img width = 640, img height = 640
Loading mode...
sdk version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) driver version: 0.9.2
model input num: 1, output num: 3
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, w_stride = 640, size_with_stride=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=0, name=output, n_dims=4, dims=[1, 255, 80, 80], n_elems=1632000, size=1632000, w_stride = 0, size_with_stride=1638400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003860
  index=1, name=283, n_dims=4, dims=[1, 255, 40, 40], n_elems=408000, size=408000, w_stride = 0, size_with_stride=491520, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=2, name=285, n_dims=4, dims=[1, 255, 20, 20], n_elems=102000, size=102000, w_stride = 0, size_with_stride=163840, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003915
model is NHWC input fmt
model input height=640, width=640, channel=3
once run use 31.119000 ms
loadLabelName ./model/coco_80_labels_list.txt
person @ (209 244 286 506) 0.884139
person @ (478 238 559 526) 0.867678
person @ (110 238 230 534) 0.824685
bus @ (94 129 553 468) 0.705055
person @ (79 354 122 516) 0.339254
loop count = 10 , average run  23.615900 ms

person @ / bus @ 为对应识别信息

rknn_yolov5_video_demo

Usage: ./rknn_yolov5_video_demo <rknn_model> <video_path> <video_type 264/265>

注意需要使用 h264/h265 码流视频

rknn_yolov5_video_demo 使用示例

$ ./rknn_yolov5_video_demo model/RK3588/yolov5s-640-640.rknn model/test.h264 264
Loading mode...
sdk version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) driver version: 0.9.2
model input num: 1, output num: 3
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=0, name=output, n_dims=4, dims=[1, 255, 80, 80], n_elems=1632000, size=1632000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003860
  index=1, name=283, n_dims=4, dims=[1, 255, 40, 40], n_elems=408000, size=408000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=2, name=285, n_dims=4, dims=[1, 255, 20, 20], n_elems=102000, size=102000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003915
model is NHWC input fmt
model input height=640, width=640, channel=3
mpi_dec_test start mpi_dec_test decoder test start mpp_type 7 app_ctx=0x7fc1305278 decoder=0x23ad0100
read video size=720664
receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 decoder require buffer w:h [1280:720] stride [1280:720] buf_size 1843200 pts=0 dts=0 decode_get_frame get info changed found receive packet size=8192 receive packet size=8192 decoder require buffer w:h [1280:720] stride [1280:720] buf_size 1843200 pts=0 dts=0 get one frame 1687102942317 data_vir=0x7f80024000 fd=13 0x24133340 encoder test start w 1280 h 720 type 7
input image 1280x720 stride 1280x720 format=2560
resize with RGA!
rga_api version 1.9.1_[4]
once run use 26.630000 ms
post process config: box_conf_threshold = 0.25, nms_threshold = 0.45
loadLabelName ./model/coco_80_labels_list.txt
car @ (380 412 438 461) 0.868004
car @ (532 362 574 399) 0.866127
car @ (496 328 530 354) 0.736625
car @ (564 311 592 331) 0.729147
car @ (860 433 928 497) 0.695733
car @ (676 306 704 327) 0.642370
car @ (738 402 792 457) 0.611211
car @ (202 441 296 498) 0.610788
bus @ (748 272 806 329) 0.527151
car @ (440 322 474 347) 0.526350
car @ (514 310 540 326) 0.507014
car @ (280 666 444 720) 0.347807
car @ (670 288 696 302) 0.271905
car @ (540 289 566 307) 0.176622
car @ (494 297 516 312) 0.168131
car @ (702 263 716 273) 0.163944

car @ / bus @ 为对应视频识别信息

3. 解析本地视频流

硬件环境

测试环境：RK3568 Debian11

目前 RK3568、RK3588 可使用此demo

示例程序测试

YOLOV5S目标检测示例程序已内置在Debian11文件系统

进入示例程序目录

$ cd /rockchip-test/npu2/rknn_yolov5_demo_Linux/

运行示例程序

$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

获取示例程序源码

$ ls external/rknpu2/examples/rknn_yolov5_video_demo/
	build  build-android_RK356X.sh  build-android_RK3588.sh  build-linux_RK356X.sh  build-linux_RK3588.sh  
	CMakeLists.txt  convert_rknn_demo  include  install  model  README.md  src

编译源码

指定交叉编译工具链路径

$ export TOOL_CHAIN=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/
$ export GCC_COMPILER=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

交叉编译工具链内置SDK目录，具体路径可参考以上命令修改

编译源码

$ cd external/rknpu2/examples/rknn_yolov5_video_demo/
$ ./build-linux_RK356X.sh

编译成功后，执行程序生成目录 install/rknn_yolov5_demo_Linux/

拷贝程序到开发板

$ ls install/rknn_yolov5_demo_Linux/
	lib/              model/            rknn_yolov5_demo

目前 rknn_yolov5_demo_Linux 的 /lib 需要使用主板上 /rockchip-test/npu2/rknn_yolov5_demo_Linux/lib
拷贝方式可用U盘、网络等
若不想要编译，可从网盘目录获取可执行程序 1-SDK Source 软件源码/demo/rknn_yolov5_demo_Linux.tar.gz

运行示例：

$ cd rknn_yolov5_demo_Linux/
$ export LD_LIBRARY_PATH=/usr/local/opencv4/lib/
$ export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/opencv4/lib/
$ export PKG_CONFIG_LIBDIR=$PKG_CONFIG_LIBDIR:/usr/local/opencv4/lib/pkgconfig
$ cp /rockchip-test/npu2/rknn_yolov5_demo_Linux/lib . -rf
$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

程序运行

程序运行命令

$ cd rknn_yolov5_demo_Linux/
$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

RK356X只能运行到7帧/秒，可自行优化程序性能

程序运行界面

4. 解析摄像头视频流

硬件环境

测试环境：RK3568 Debian11

目前 RK3568、RK3588 可使用此demo

示例程序测试

YOLOV5S目标检测示例程序已内置在Debian11文件系统

进入示例程序目录

$ cd /rockchip-test/npu2/rknn_yolov5_demo_Linux/

运行示例程序

$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn /dev/video10

运行时间过长会因内存不足，终止进程

获取示例程序源码

与解析本地视频流使用demo为同一个，故源码参考解析本地视频流

Last updated 13 days ago

30-Linux NPU YOLOV5S 目标检测

1. NPU YOLOV5S 是什么

2. 解析图片及视频

RK Linux官方提供rknn_yolov5_demo，源码支持 RK3562、RK3566、RK3568、RK3588，编译出工具可进行图像解析和视频解析

源码路径

(SDK)/external/rknpu2/examples/rknn_yolov5_demo

源码参考及编译

配置环境

$ export TOOL_CHAIN=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/
$ export GCC_COMPILER=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

SDK目录为指代实际中要替换成自己的实际目录

编译对应的工具

$ cd external/rknpu2/examples/rknn_yolov5_demo/
$ ./build-linux_RK3562.sh

build-android_RK3562.sh build-android_RK3566_RK3568.sh build-android_RK3588.sh build-linux_RK3562.sh build-linux_RK3566_RK3568.sh build-linux_RK3588.sh
根据实际IC选择对应的脚本进行编译

生成工具路径

rknn_yolov5_demo$ ls install/rknn_yolov5_demo_Linux/
lib  model  rknn_yolov5_demo  rknn_yolov5_video_demo

拷贝到主板上运行使用

工具使用

rknn_yolov5_demo

Usage: ./rknn_yolov5_demo <rknn model> <jpg>

rknn_yolov5_demo 使用示例

$ ./rknn_yolov5_demo model/RK3588/yolov5s-640-640.rknn model/bus.jpg
post process config: box_conf_threshold = 0.25, nms_threshold = 0.45
Read model/bus.jpg ...
img width = 640, img height = 640
Loading mode...
sdk version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) driver version: 0.9.2
model input num: 1, output num: 3
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, w_stride = 640, size_with_stride=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=0, name=output, n_dims=4, dims=[1, 255, 80, 80], n_elems=1632000, size=1632000, w_stride = 0, size_with_stride=1638400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003860
  index=1, name=283, n_dims=4, dims=[1, 255, 40, 40], n_elems=408000, size=408000, w_stride = 0, size_with_stride=491520, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=2, name=285, n_dims=4, dims=[1, 255, 20, 20], n_elems=102000, size=102000, w_stride = 0, size_with_stride=163840, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003915
model is NHWC input fmt
model input height=640, width=640, channel=3
once run use 31.119000 ms
loadLabelName ./model/coco_80_labels_list.txt
person @ (209 244 286 506) 0.884139
person @ (478 238 559 526) 0.867678
person @ (110 238 230 534) 0.824685
bus @ (94 129 553 468) 0.705055
person @ (79 354 122 516) 0.339254
loop count = 10 , average run  23.615900 ms

person @ / bus @ 为对应识别信息

rknn_yolov5_video_demo

Usage: ./rknn_yolov5_video_demo <rknn_model> <video_path> <video_type 264/265>

注意需要使用 h264/h265 码流视频

rknn_yolov5_video_demo 使用示例

$ ./rknn_yolov5_video_demo model/RK3588/yolov5s-640-640.rknn model/test.h264 264
Loading mode...
sdk version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) driver version: 0.9.2
model input num: 1, output num: 3
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=0, name=output, n_dims=4, dims=[1, 255, 80, 80], n_elems=1632000, size=1632000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003860
  index=1, name=283, n_dims=4, dims=[1, 255, 40, 40], n_elems=408000, size=408000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=2, name=285, n_dims=4, dims=[1, 255, 20, 20], n_elems=102000, size=102000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003915
model is NHWC input fmt
model input height=640, width=640, channel=3
mpi_dec_test start mpi_dec_test decoder test start mpp_type 7 app_ctx=0x7fc1305278 decoder=0x23ad0100
read video size=720664
receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 receive packet size=8192 decoder require buffer w:h [1280:720] stride [1280:720] buf_size 1843200 pts=0 dts=0 decode_get_frame get info changed found receive packet size=8192 receive packet size=8192 decoder require buffer w:h [1280:720] stride [1280:720] buf_size 1843200 pts=0 dts=0 get one frame 1687102942317 data_vir=0x7f80024000 fd=13 0x24133340 encoder test start w 1280 h 720 type 7
input image 1280x720 stride 1280x720 format=2560
resize with RGA!
rga_api version 1.9.1_[4]
once run use 26.630000 ms
post process config: box_conf_threshold = 0.25, nms_threshold = 0.45
loadLabelName ./model/coco_80_labels_list.txt
car @ (380 412 438 461) 0.868004
car @ (532 362 574 399) 0.866127
car @ (496 328 530 354) 0.736625
car @ (564 311 592 331) 0.729147
car @ (860 433 928 497) 0.695733
car @ (676 306 704 327) 0.642370
car @ (738 402 792 457) 0.611211
car @ (202 441 296 498) 0.610788
bus @ (748 272 806 329) 0.527151
car @ (440 322 474 347) 0.526350
car @ (514 310 540 326) 0.507014
car @ (280 666 444 720) 0.347807
car @ (670 288 696 302) 0.271905
car @ (540 289 566 307) 0.176622
car @ (494 297 516 312) 0.168131
car @ (702 263 716 273) 0.163944

car @ / bus @ 为对应视频识别信息

3. 解析本地视频流

硬件环境

测试环境：RK3568 Debian11

目前 RK3568、RK3588 可使用此demo

示例程序测试

YOLOV5S目标检测示例程序已内置在Debian11文件系统

进入示例程序目录

$ cd /rockchip-test/npu2/rknn_yolov5_demo_Linux/

运行示例程序

$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

获取示例程序源码

$ ls external/rknpu2/examples/rknn_yolov5_video_demo/
	build  build-android_RK356X.sh  build-android_RK3588.sh  build-linux_RK356X.sh  build-linux_RK3588.sh  
	CMakeLists.txt  convert_rknn_demo  include  install  model  README.md  src

编译源码

指定交叉编译工具链路径

$ export TOOL_CHAIN=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/
$ export GCC_COMPILER=SDK目录/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

交叉编译工具链内置SDK目录，具体路径可参考以上命令修改

编译源码

$ cd external/rknpu2/examples/rknn_yolov5_video_demo/
$ ./build-linux_RK356X.sh

编译成功后，执行程序生成目录 install/rknn_yolov5_demo_Linux/

拷贝程序到开发板

$ ls install/rknn_yolov5_demo_Linux/
	lib/              model/            rknn_yolov5_demo

目前 rknn_yolov5_demo_Linux 的 /lib 需要使用主板上 /rockchip-test/npu2/rknn_yolov5_demo_Linux/lib
拷贝方式可用U盘、网络等
若不想要编译，可从网盘目录获取可执行程序 1-SDK Source 软件源码/demo/rknn_yolov5_demo_Linux.tar.gz

运行示例：

$ cd rknn_yolov5_demo_Linux/
$ export LD_LIBRARY_PATH=/usr/local/opencv4/lib/
$ export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/opencv4/lib/
$ export PKG_CONFIG_LIBDIR=$PKG_CONFIG_LIBDIR:/usr/local/opencv4/lib/pkgconfig
$ cp /rockchip-test/npu2/rknn_yolov5_demo_Linux/lib . -rf
$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

程序运行

程序运行命令

$ cd rknn_yolov5_demo_Linux/
$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn model/test.mp4

RK356X只能运行到7帧/秒，可自行优化程序性能

程序运行界面

4. 解析摄像头视频流

硬件环境

测试环境：RK3568 Debian11

目前 RK3568、RK3588 可使用此demo

示例程序测试

YOLOV5S目标检测示例程序已内置在Debian11文件系统

进入示例程序目录

$ cd /rockchip-test/npu2/rknn_yolov5_demo_Linux/

运行示例程序

$ ./rknn_yolov5_demo model/RK356X/yolov5s-640-640.rknn /dev/video10

运行时间过长会因内存不足，终止进程

获取示例程序源码

与解析本地视频流使用demo为同一个，故源码参考解析本地视频流

Last updated 13 days ago