Graduation Project

Title: Multi-objective detection and instance segmentation of shield tunnel diseases based on deep learning

We utilize yolov5-7.0 to perform the object detection and instance segmentation tasks for shield tunnel diseases, for example, cracks, damages, and seepage.

Documentation

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

git clone https://github.com/JiwenJ/Graduation-project.git  # clone
cd yolov5
pip install -r requirements.txt  # install

Dataset preparation

dd

Train from scratch

Train with transfer learning

Inference

Weights

According to our experiments, we conclude the object detection results in the following tables:

编号	加入注意力机制	修改特征金字塔	修改损失函数	[email protected]	[email protected]:0.95	权重下载
1	✗	✗	✗	72.5	43.1	[Google Drive]
2	✓	✗	✗	71.4	35.3	[Google Drive]
3	✗	✓	✗	74.6	40.6	[Google Drive]
4	✗	✗	✓	78.9	47.8	[Google Drive]
5	✓	✓	✓	80.1	48.0	[Google Drive]

1: What we refer to is YOLOv5s-7.0.

The best combination for object detection in our experiments is YOLOv5 with BiFPN, SIoU and AM. You can download the weight from [Google Drive].

Improvement

Result

Contact

• E-mail: [email protected]

Acknowledgement

Dataset is credited to NJU.
Computation resource is credited to NJU and CASIA.
Computer vision tutorial is credited to online resources and RDD competition.