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YOLOP_RESA

[TOC]

1, Introduce

In this project, we aim to make a Lane_detection System for Self Driving Car with Instance Segmentation Approach. This system can detect not only weather each pixel is a part of Lanes but which Lane it belongs to.

2, Structure

We use Instance Segmentaion as our main approach, We apply CNN to deal with our task. Our CNN Net consist of two part:

  • YOLOP net (CSPDarknet with SPP structure)
  • RESANET (RESA and BUSDecoder)

3, Training

▲Preparing

Our Training data need to be labelled in the form of Tusimple Dataset ,which means your dataset must consist of two part: your gt_image and json file.

▲Step 1

With the labelled data, your need to put your dataset folder in ./data and create another three .json file -- test_label.json and valset.json

data/your_data_folder 
    |-gt_image/ 
    |-JsonFile_label.json
    |-test_label.json
    |-valset.json

▲Step 2 prepare testing set

then, cut (or copy ) some lines in JsonFile_label and paste them in test_label.json and valset.json

▲Step 3 generating segmentation labels

python tools/generate_seg_tusimple.py --root ./data/your_data_folder

▲Step 4 modify config python file

1.set your training path

go to ./lib/config/resa_tusimple.py, find "dataset_path" and "test_json_file" and change their content with './data/your_data_folder' and './data/your_data_folder/test_label.json' respectively.

▲Step 5 training

Start training using following comment:

python tools/train.py --view --cpu 0 --load_from your/pretrained/weight/path

4,testing

python tools/train.py --view -- validate --cpu 0 --load_from your/pretrained/weight/path