BS-YOLOV5S: INSULATOR DEFECT DETECTION WITH ATTENTION MECHANISM AND MULTI-SCALE FUSION

This is the project code file in the paper. After installing the necessary running environment, the experimental results in the paper can be reproduced. We provide the trained weights file best.pt used in the paper, which was trained on the WI dataset.

Quick Start Examples

Install

First, clone the project and configure the environment. Python>=3.7.0, PyTorch>=1.7.

git clone https://github.com/jspron/insulator-defect  # clone
cd insulator-defect
pip install -r requirements.txt  # install

Train

The project uses yolov5s.pt as the pre-trained model, make sure to get it before you start training. The dataset WI we used is placed in /root/datasets directory, so make sure the dataset directory is correct during training. Then start the training with the following command:

python train.py --weights yolov5s.pt --cfg models/BS-yolov5s.yaml --data data/mydata.yaml

Test

We have provided the trained weights file described in the paper, best.pt. After setting up the deep learning environment and preparing the WI dataset, the results can be reproduced using the following command:

python val.py --data data/mydata.yaml --weights best.pt --task test

Weather-Insulator

The WI dataset can be obtained at (https://pan.baidu.com/s/1lgG6BX1Ac9b8_gAwSMOQ0g) with (j8cx).