4 Mins Read

Automated corrosion detection with Tensorflow object detection API

Automated corrosion detection

Automated corrosion detection with Tensorflow object detection API

Corrosion on a bridge
Figure1. Corrosion on a bridge

Visual inspection is a crucial part of property management, which is done to find apparent defects. By using image processing methods, we can automate corrosion inspections and speed up this process, mainly in critical scenarios like ship, bridge, and pipeline inspections.

In this article, we describe what corrosion is and why it occurs, then we introduce tensor flow object detection API and use it to detect corrosion in images.

 

What is Corrosion and Rusting?

Corrosion is one of the most common popular we encounter in our daily lives. You’ve probably noticed that over time, some iron items become coated in an orange or reddish-brown colored coating. This layer is made by a chemical reaction called rusting, which is a type of corrosion. Rusting is a kind of oxidation. Rust is formed when iron interacts with water and oxygen. The main causes of corrosion are:

  • Weather conditions: Metals exposed to environmental elements such as water, wind, and moisture will oxidize and rust the metal surface.
  • Harmful gases and chemicals: Corrosive substances such as sulfur oxides, hydrogen oxides, acids, bases, etc. also promote corrosion of certain electrical equipment.
  • Biological Substances: Dirt and bacteria also cause corrosion in metals.

Common industries affected by corrosion

Corrosion of metals can happen in various industries. Corrosion causes metals to not have their previous performance and, in some industries, this is very dangerous. Therefore, checking the corrosion of metals is very important in the equipment monitoring. Here’s a list of industries in which corrosion are commonly occur:

  • Transportation pipelines: Pipeline is mainly used for oil and gas transportation, and corrosion occurs on both the inside and outside of any pipe.
  • Ship: Because of the environment in which they operate, ships are among the structures most vulnerable to environmental corrosion. Seawater is a very corrosive environment due to the salt it contains.
  • Railway: In the long days of wind and sun, rails will be rusting, which will undoubtedly cause certain safety hazards.
  • Boilers: Boiler corrosion generally occurs when the alkalinity of the boiler water is low or when the metal is exposed to oxygenated water, it can cause deep holes in the metal cladding.
  • bridge maintenance: On bridges, corrosion is most often caused when steel is exposed to atmospheric conditions, such as salt, moisture, and oxygen
  • electric pole: Corrosion begins to consume a transmission tower or pole, especially in humid weather.

Prevention is better than repair

Corrosion Monitoring is a procedure that evaluates and monitors apparatus components, structures, procedure units, and facilities for traces of corrosion. Monitoring programs aim to identify certain situations in mandate to extend the life and serviceability of assets while increasing safety and reducing replacement costs. Corrosion monitoring covers all kinds of corrosion and materials

There are three main reasons it’s important to use a corrosion monitoring system:

  • Safety
  • Cost reduction
  • Improved efficiency

We can use image processing for automated detection of corrosion from images or video presents significant benefits in terms of corrosion monitoring. Advantages include access to remote locations, inspector safety, cost savings, and speed in monitoring.

Gathering Images

Before introducing TensorFlow Object Detection API and using it to detect corrosion, we must collect several images of different corrosion and then annotate them. These images will be used to train our model.

Annotation of corrosion on ship
Figure2. Annotation of corrosion on ship

 

Annotation of corrosion on car
Figure3. Annotation of corrosion on car

step by step TensorFlow Object Detection API

  • Install tensorflow
  • Download, install and compile Protobuf
  • Download and extract TensorFlow Object Detection API
  • Split images to three files train, test, validation
  • Create label_map.pbtxt
  • Generate Tfrecords for all three folders
  • Choose a model architecture to work with
  • Go to the TF 2 Detection Model Zoo page and download it
  • Now we are ready to train our model
  • Can check model training via tensorboard
  • The last step is to test our model
Figure4. Corrosion
Related articles
Trauma Detection on Pelvic Radiographs using Computer Vision Algorithms
A timely and accurate diagnosis of the proximal femur and pelvis injuries in trauma patients is essential to saving...
Defect-Detection-in-Metal-Parts-using-Optical-Systems
Detecting and classifying aesthetic defects in different sizes, shapes, and positions immediately after the casting process is an essential...
deep learning
Nowadays with the help of computer vision technology and image processing we can classify broken and normal bone X-ray...
Annotating Computer Vision Projects
Choosing the right images for training, validating, and testing computer vision algorithms will significantly affect your AI project's success....
Metaverse
Metaverse is one of the fastest-growing technologies today. A comprehensive review of computer vision concepts in Metaverse is presented...
casting defects
Automated X-ray systems are improving quality through multiple objective inspections, and reducing labor costs, and increasing productivity and consistency...
Subscribe to our newsletter and get the latest practical content.

You can enter your email address and subscribe to our newsletter and get the latest practical content. You can enter your email address and subscribe to our newsletter.