This is the joint training model for traffic sign detection and image denoising proposed in our paper titled "CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions".

UPDATE 2024.3.15

Our paper was accepted by the 2024 International Joint Conference on Neural Networks (IJCNN 2024 Oral).

UPDATE 2024.8.25

To make using our methodology easier, we uploaded our full dataset CCTSDB-AUG on the KAGGLE platform (Please cite our paper and dataset).

You can also compare our metrics in Paper with Code.

Project Guidence

The image denoising module of our model utilizes the 4kDehazing model(cite: https://github.com/zzr-idam/4KDehazing.git), while the object detection module incorporates the improved model CCSPNet, which is based on the YOLOv5 baseline, as proposed in our article. This model is a joint training model, and each training session will generate two pth files: 'best.pt' for the object detection model and 'best_4k.pt' for the image denoising model.

The proposed method and comparisons in this paper were conducted under a unified data augmentation approach. To replicate the experiments, you will need to download the dataset and pre-trained weights and place them in a specific directory. Then, in the terminal, run the command：python train_ccspnet_joint.py --rect

It is worth noting that the joint training model defines a joint loss function calculation formula as loss = alpha * loss1 + beta * loss2, where alpha and beta are hyperparameters. Through extensive experimentation, it has been found that setting alpha = beta = 0.5 yields good results.

The repository includes:

1.CCSPNet model:

 CCSPNet-Joint/models/yolov5l-efficientvit-b2-cot.yaml

2.pretrained_pth:

 Download link：[https://pan.baidu.com/s/1wfMUxK3Z09R00wus3XzVEA](https://pan.baidu.com/s/1Vo-Xe07KtYYm5TF9Vx4DSQ) 
 Verification code：1rvo 
 Content:
 ccspnet-joint.pt
 our_deblur40.pth
 resnet50-0676ba61.pth
 
 checkpoints\efficientViT\b2-r288.pt: 
 Download link: https://pan.baidu.com/s/1gmXAfND0roMpjCeLO4htlg    Verification code：tl7e 

3.Dataset:

CCTSDB: https://github.com/csust7zhangjm/CCTSDB.git
Augment method for CCTSDB-AUG:  StimulateExtreme.py
We also provide our whole dataset in KAGGLE.

Please cite our work:

@INPROCEEDINGS{10651346,
  author={Hong, Haoqin and Zhou, Yue and Shu, Xiangyu and Hu, Xiaofang},
  booktitle={2024 International Joint Conference on Neural Networks (IJCNN)}, 
  title={CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions}, 
  year={2024},
  pages={1-8},
  doi={10.1109/IJCNN60899.2024.10651346}}