https://github.com/enesozi/object-detection
Object detection on thermal images(FLIR dataset)
https://github.com/enesozi/object-detection
darknet object-detection thermal-imaging
Last synced: 7 months ago
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Object detection on thermal images(FLIR dataset)
- Host: GitHub
- URL: https://github.com/enesozi/object-detection
- Owner: enesozi
- License: mit
- Created: 2019-04-20T14:45:28.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-04T10:08:46.000Z (about 6 years ago)
- Last Synced: 2024-11-01T00:32:41.524Z (12 months ago)
- Topics: darknet, object-detection, thermal-imaging
- Language: Makefile
- Homepage:
- Size: 736 KB
- Stars: 90
- Watchers: 7
- Forks: 26
- Open Issues: 4
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Object Detection
Object detection on thermal images
### Steps to follow:
* **./build_docker_container.sh** (To build an nvidia-docker)
* **./run_docker_container.sh** (To run the built nvidia-docker by name "darknet_thermal" and with mounted dataset.
* Make sure that your gpu arch is included in [Makefile](https://github.com/enesozi/object-detection/blob/master/Makefile#L16)
* If it's not, then add your gpu arch and run **make clean** and **make** commands in darknet directory.
* **./preprocess_flir_dataset.sh** (Make sure that image directories are consistent with yours.)
* Exit the container by using "**Ctrl+P and Q**". This leaves the container still running.
* Start training in detached mode by using the following command:
* **nvidia-docker exec -d darknet_thermal bash -c "cd /home/object-detection/ ; ./preprocess_flir_dataset.sh ; ./start_training.sh"**
* In **start_training.sh** script gpu id is 3 by default. You might need to adjust this according to yours.
#### PyCoco Results for IoU=0.50, area=all, maxDets=100
Average Precision (AP) @[ IoU=0.50:0.50 | area= all | maxDets=100 ] = **0.714**
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.50 | area= small | maxDets=100 ] = 0.576
Average Precision (AP) @[ IoU=0.50:0.50 | area=medium | maxDets=100 ] = 0.819
Average Precision (AP) @[ IoU=0.50:0.50 | area= large | maxDets=100 ] = 0.906
Average Recall (AR) @[ IoU=0.50:0.50 | area= all | maxDets= 1 ] = 0.348
Average Recall (AR) @[ IoU=0.50:0.50 | area= all | maxDets= 10 ] = 0.781
Average Recall (AR) @[ IoU=0.50:0.50 | area= all | maxDets=100 ] = **0.787**
Average Recall (AR) @[ IoU=0.50:0.50 | area= small | maxDets=100 ] = 0.719
Average Recall (AR) @[ IoU=0.50:0.50 | area=medium | maxDets=100 ] = 0.834
Average Recall (AR) @[ IoU=0.50:0.50 | area= large | maxDets=100 ] = 0.918
Baseline result: mAP IoU(0.5) of 0.587
You can download the dataset from [here](https://mega.nz/#!j9l32aAJ!wB4pk6H_12AaCRZT5flmNKcBcpCDdfleTaMi4WA8_-0)
You can find the [blog post](https://medium.com/swlh/object-detection-on-thermal-images-4f3410a89db4) published on Medium.
Pretrained weights: [thermal](https://mega.nz/#!vk9HDICC!qK13x8bjF1zY2aIJalR6BIZ1yfQye_r1NLcTxUJGNEs)