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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

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.