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
  • 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

You can find the blog post published on Medium.

Pretrained weights: thermal