Aquarium Object Detection 🐠

Underwater Health Monitoring is an essential way to prevent extinction of sea animals and coral reef. In this repository, we will build an aquarium object detection system using Deep Learning and Computer Vision.

Dataset

The Aquarium Object Detection Dataset is collected by Brad Dwyer(Roboflow team) from two aquariums in the United States: The Henry Doorly Zoo in Omaha (October 16, 2020) and the National Aquarium in Baltimore (November 14, 2020). The dataset consists of 638 images splitted into train, test and validation data.

Model

We will be using EfficientDet D0 model from TensorFlow 2 Detection Model Zoo. They provide a collection of detection models pre-trained on the COCO 2017 dataset.

Model name	Speed (ms)	COCO mAP	Outputs
EfficientDet D0 512x512	39	33.6	Boxes

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Metrics

After training for 4300 steps:

{'Loss/classification_loss': 0.18720163,
 'Loss/localization_loss': 0.08831813,
 'Loss/regularization_loss': 0.04052207,
 'Loss/total_loss': 0.31604183,
 'learning_rate': 0.07999277}

Validation Detection Metrics:

Accumulating evaluation results...
DONE (t=0.19s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.324
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.626
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.298
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.022
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.226
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.443
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.178
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.374
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.449
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.372
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.554

Tensor Board Metrics

References

Custom object detection in the browser using TensorFlow.js by Hugo Zanini
TensorFlow Object Detection API Tutorial
TensorFlow 2 Detection Model Zoo