[vc_row row_type=”3″ blox_image=”21416″ align_center=”aligncenter” page_title=”page-title-x” blox_dark=”true” parallax_speed=”8″][vc_column]

---

---

[vc_column_text]

Car properties and damage recognition for an insurance company

[/vc_column_text]

---

---

[/vc_column][/vc_row][vc_row][vc_column width=”1/4″]

---

---

[/vc_column][vc_column width=”1/2″]

---

[vc_column_text]The project was implemented for one of the largest insurance companies in Georgia. The final goal of the project was automatization of car damage estimation and integration API with the company’s mobile app so customers can insure their cars. [/vc_column_text]

---

[/vc_column][vc_column width=”1/4″][/vc_column][/vc_row][vc_row][vc_column width=”1/3″][vc_single_image image=”21417″ img_size=”550×350″][/vc_column][vc_column width=”1/3″][vc_single_image image=”21418″ img_size=”550×350″][/vc_column][vc_column width=”1/3″][vc_single_image image=”21419″ img_size=”550×350″][/vc_column][/vc_row][vc_row][vc_column]

---

[vc_column_text]

About this project

[/vc_column_text][vc_row_inner][vc_column_inner width=”1/2″][vc_column_text]Project Description

We have developed a neural network to recognize the exact damaged area of the car from the uploaded image. We have labeled 50 000 car images that made it possible to detect: flat tires, broken windows, dent, vandalism, and crashes. Also, our machine learning model can recognize a brand of the car and determine from which angle the picture was taken. Our client used the model to create a mobile app, enabling users to insure their cars without the involvement of an insurance agent. [/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text]Technologies:

• PyTorch based convolutional neural networks (Mask-R CNN) + scikit-learn / XGBoost for car properties and damage detection and price estimation.
• Scrapy for collecting data.

[/vc_column_text][/vc_column_inner][/vc_row_inner]

---

[/vc_column][/vc_row]