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Inspection and identification algorithm based on improved SOLOv2 of intelligent robot in complex environment

On the basis of SOLOv2, it is improved and optimized according to the needs of intelligent inspection robots. It can identify 15 types of equipment components and perform corresponding post-processing, with an accuracy of more than 90%.

This code of this project is mainly modified and summarized based on SOLO, if there is any error, please correct me! ! !

Highlights

• Multi-category part recognition in complex scenes: For complex distribution station scenarios, up to 15 types of equipment components can be identified, and status information can be obtained for each type of equipment components;
• Small object part recognition: By optimizing the feature output of the feature pyramid, the recognition accuracy of small targets is improved, and the overall accuracy is slightly improved.

Installation

This implementation is based on mmdetection(v1.0.0).The installation method is as follows, also can refer to the original INSTALL.md for installation and dataset preparation.

Requirements

• Linux (Windows is not officially supported)
• Python 3.5+
• PyTorch 1.1 or higher (>=1.5 is not tested)
• CUDA 9.0 or higher
• NCCL 2
• GCC 4.9 or higher
• mmcv 0.2.16

The software and hardware versions implemented in this project are as follows:

• OS: Ubuntu 18.04
• CUDA: 10.2.89
• CUDNN: 7.6.5
• NCCL: 2.8.3
• GCC(G++): 7.5.0

Install SOLO

a. Create a conda virtual environment and activate it.

conda create -n solo python=3.7 -y
conda activate solo

b. Install PyTorch and torchvision following the official instructions, e.g.,

pip install torch==1.4.0 torchvision==0.5.0
pip install cython

c. Clone the SOLO repository.

git clone https://github.com/WXinlong/SOLO.git
cd SOLO

d. Install build requirements and then install SOLO.

pip install -r requirements/build.txt
pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
pip install -v -e .  # or "python setup.py develop"

e. The SOLO environment is compiled and the GPU version of paddlepaddle is installed.

pip install paddlepaddle-gpu==2.0.1

Usage

A quick demo

Once the installation is done, you can download the provided models and use inference_demo.py to run a quick demo.

Train SOLO

# Train with single GPU
python tools/train.py ${CONFIG_FILE}

# Example
python tools/train.py configs/solo/solo_r50_fpn_8gpu_1x.py

Testing SOLO

# single-gpu testing
python tools/test_ins.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --out  ${OUTPUT_FILE} --eval segm

# Example
python tools/test_ins.py configs/solo/solo_r50_fpn_8gpu_1x.py  SOLO_R50_1x.pth --show --out  results_solo.pkl --eval segm

Demonstration of Intelligent Patrol Inspection and Recognition

Please refer to README.md