Case Study: DeepPlastic and YOLOv5

Plastic is suffocating marine wildlife: every minute, two truckloads of plastic are dumped into our oceans, equating to over 10 million tons a year. DeepPlastic scientists state that this marine plastic poses societal threats to the "marine environment, food safety, human health, eco-tourism, and contributions to climate change."

To combat this, this team of researchers and engineers have been investigating how computer vision can eliminate plastic in our oceans.

With deep learning technology, DeepPlastic researchers have developed an approach that uses autonomous underwater vehicles (AUVs) to scan, identify, and quantify plastic located just below the surface of the ocean where light can still penetrate through, or the Epipelagic layer.

“Our goal was to have a very tiny model with a very fast inference speed that can be used to detect plastic.” Jay Lowe, Machine Learning Researcher

The DeepPlastic team trained two small and precise models, YOLOv4 and YOLOv5, allowing for real-time object detection. These models were trained on the DeepTrash dataset, which consisted of:

• 1900 training images, 637 test images, 637 validation images (60, 20, 20 split)
• Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA.
• Internet images

“We were looking for an object detection algorithm that produces high accuracy and is extremely fast. The oceanic environments that we work in are harsh, rough terrains. YOLOv5 delivered on all fronts as the best object detection model we could’ve used.” “We love using YOLOv5 since it’s so easy to set up and use, and it has been producing the results that we’ve wanted consistently.” “For any future models that we will deploy, we will be looking at YOLOv5 as our first choice without a shadow of a doubt.” Gautam Tata, Machine Learning Researcher

Check out the DeepPlastic repository, published paper, and video recap.