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RK3588 车牌检测与识别 RKNPU推理

介绍

基于yolov5-face-RK3588,在RK3588上运行car recognition检测模型,实现车辆+车牌检测,并输出车牌的4个landmark点,用于车牌识别之前的矫正.

环境依赖

  • RK3588 Debian11
  • rknntoolit 1.6.0
  • rknnnpu 1.6.0
  • opencv4+

模型

python 使用的模型尺寸

输入： 1 3 640 640
输出：
1 3 80 80 16
1 3 40 40 16
1 3 20 20 16

后处理之前的模型输出结构说明

16位分别对应: [x, y, w, h , conf, x1, y1, x2, y2, x3, y3, x4, y4, cls0, cls1, cls2] 其中检测标签:

  • cls0: 单层车牌
  • cls1: 双层车牌
  • cls2: 车

python推理测试

实现了车牌检测, 车牌矫正,双层和单层车牌的预处理以及车牌识别

# python
cd python
python plate_detection_rec_rknnlite.py --input ../imgs/double_yellow.jpg

result

C++代码推理测试

cd cpp
bash build-linux_RK3588.sh # 在RK3588上直接编译
cd build
./rknn_yolov5_demo  ../../weights/car_plate_detect_1x3x80x80x16-640-640_rm_transpose_rk3588.rknn ../../imgs/沪A5L320.jpg 

# 运行结果:
post process config: box_conf_threshold = 0.25, nms_threshold = 0.45
Loading mode...
sdk version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11) driver version: 0.9.2
model input num: 1, output num: 3
  index=0, name=input, 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=5, dims=[1, 3, 80, 80, 16], n_elems=307200, size=307200, w_stride = 0, size_with_stride=307200, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=63, scale=0.176689
  index=1, name=975, n_dims=5, dims=[1, 3, 40, 40, 16], n_elems=76800, size=76800, w_stride = 0, size_with_stride=76800, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=44, scale=0.161176
  index=2, name=987, n_dims=5, dims=[1, 3, 20, 20, 16], n_elems=19200, size=19200, w_stride = 0, size_with_stride=19200, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=84, scale=0.329176
model is NHWC input fmt
model input height=640, width=640, channel=3
Read ../../imgs/沪A5L320.jpg ...
img width = 1920, img height = 1080
resize image with letterbox
once run use 35.621000 ms
loadLabelName ../../weights/plate.txt
car @ (465 279 1059 735) 0.875814
Single-L @ (762 672 882 720) 0.867393
save detect result to ./out.jpg
loop count = 10 , average run  29.781800 ms

detection result

模型转换说明

在导出onnx模型时，由于输出的尺度不一样，特别说明一下，截断得到3输出，尺寸是(1, 3, 80, 80, 16)

Car_recognition/yolo.py

 45     def forward(self, x):
 46         # x = x.copy()  # for profiling
 47         z = []  # inference output
 48         if self.export_cat:
 49             for i in range(self.nl):
 50                 x[i] = self.m[i](x[i])  # conv
 51                 bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
 52 
 53                 x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
 54             return x

备注： 如果需要导出其他的尺度，需要修改52行代码，将view的尺寸修改为相应的要求来适配：(bs, self.na, self.no, ny, nx)

参考