hankerkuo/vehicle-front-rear-detection-for-license-plate-detection-enhancement

A Network for detecting and classifying vehicle's front and rear

vehicle-front-rear-detection-for-license-plate-detection-enhancement's Issues

rear detected as front?

Hello, I am using YOLO3-tiny, please try image below, and it detects as "front". While the normal YOLO3 detects it correctly as "rear". Thanks.

Error in running Front_Rear_Detect.py

Hi
I ran Front_Rear_Detect.py on Google Colab using:
!python2.7 Front_Rear_Detect.py
and I am getting this error:

Traceback (most recent call last):
  File "Front_Rear_Detect.py", line 58, in <module>
    FRs, cate = fr_detect(img)
TypeError: 'NoneType' object is not iterable
			detecting front and rear using FRD..., Model: data/FRD/FRNet_YOLOv3.cfg
			FR detection failed

Can you tell me how to fix this. Thank you

which dataset use for Front and Rear Detection

which dataset use for front and rear detection

harmony.cs.pusan.ac.kr/~nrlab/2019MITA/Vehicle-Front-Rear not available

help to run this program

Can you please tell me how to run this program?
Which editor and all changes to be made please.
Thank you

how to use in keras not use darknet

I need to run model in keras without Darknet aslo how can I covert model weight and cofig file for keras model

Could you help please with AlexeyAB / darknet

Hello!
First, thank you so much for your work and this solution!
I'm trying to use TensorRT version of darknet on Jetson Xavier, instead of https://github.com/pjreddie/darknet,
but getting NULL Pointer error. Could you please explain and give advice if it possible to resolve this issue

Thank you so much in advance!

root@jetsonNX:/home/user/Vehicle-Front-Rear-Detection-for-License-Plate-Detection-Enhancement# python Front_Rear_Detect.py
FRD Net pre-loading...
Try to load cfg: data/FRD/FRNet_YOLOv3_tiny.cfg, clear = 0
0 : compute_capability = 720, cudnn_half = 1, GPU: Xavier
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 1
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 21 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 21 0.004 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 21 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 21 0.007 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.449
avg_outputs = 325057
Allocate additional workspace_size = 38.79 MB
Try to load weights: data/FRD/FRNet_YOLOv3_tiny_126000.weights
Loading weights from data/FRD/FRNet_YOLOv3_tiny_126000.weights...
seen 64, trained: 8064 K-images (126 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Loaded - names_list: data/FRD/FRNet.names, classes = 2
2021-06-17 22:57:35.861504
('\t\t\tdetecting front and rear using FRD..., Model:', 'data/FRD/FRNet_YOLOv3_tiny.cfg')
Traceback (most recent call last):
File "Front_Rear_Detect.py", line 68, in
FRs, cate = fr_detect(img, start)
File "Front_Rear_Detect.py", line 32, in fr_detect
results, wh = dn.detect(FR_net, FR_meta, img, threshold)
File "/home/user/Vehicle-Front-Rear-Detection-for-License-Plate-Detection-Enhancement/darknet/python/darknet.py", line 160, in detect
if dets[j].prob[i] > 0:
ValueError: NULL pointer access