Automotive industry is an important sector of the economy and it is characterized by a high degree of innovation. Heavy industry has a significant impact on the economy, culture and society.
The automotive industry products are sold in more than one hundred countries on five continents. It`s permanent innovation is reflected in the constant renewal of its products as well as in ongoing improvements to their quality and safety. Quality standards are strictly respected, and thus the need for tools to automate QC inspection, like vehicle damage detectors.
Car industry generates jobs and income, and serves as an engine for economic growth and development. This industry has experienced exponential growth over recent years due to its ability to adapt to new technologies and new demands from consumers around the world. In addition, it also has an impact on the environment since it produces low levels of pollution compared to other industrial sectors. Quality in manufacture has also developed along side, and in-process inspection of units it´s a must. It used to be done by inspectors, which was time and effort consuming, and inaccurate as well. With the new technologies, AI and ML today there are tools like Vehicle Damage Detector API that can scan and detect flaws in real time.
Automotive Industry: Sensors And Controls
The automotive sector uses many different types of sensors, such as light sensors, proximity sensors, GPS sensors, laser sensors, etc. These sensors are used both in vehicles and in infrastructure (roads). In recent years there has been a significant increase in demand for these sensors as a result of their use in autonomous vehicles.
The early detection of defects in the units produced saves further issues. Quality controls are fast and easier with Vehicle Damage Detector API. This can be especially important on working with fleets or renting cars. Detection, identification, location and assessment of the damage will guarantee that every unit leaves the plant in excelent roadworthy condition, which contributes to the reliability of the cars and the prestige of the brand.
An image analysis API can help automatically detect damage in a picture and even determine its severity. This can be very helpful for a number of reasons, but one of the most important is that it can help save time and money. By automating the damage detection process, the inspection is eased down and faster, as well as accurate minimizing the traditional possibility of human error when it was done manually.
For developers who are looking for a cost-effective way to devise tools for their clients in the automotive industry, so that they can improve the damage detection process, nothing better than Vehicle Damage Detector API. This API is designed specifically for use in the automotive industry and is easy to use and integrated into any existing systems, allowing to perform check-ups without stopping the conveyor line, and early rectification of flaws, if detected. The response comes in real-time, a feature that makes this tool absolutely indispensable.
This software is efficient t to automatically categorize images based on their content, which can be helpful even when there´s a large number of images that need to be inspected.
It’s also very affordable, so it’s a great option for any size of business.
How Does This API Work?
Counting on a subscription on Zyla API Hub marketplace, just start using, connecting and managing APIs. Subscribe to Vehicle Damage Detector API by simply clicking on the button “Start Free Trial”. Then meet the needed endpoint and simply provide the picture of the product. Make the API call by pressing the button “test endpoint” and see the results on display. The AI will process and retrieve an accurate report using this data.
It`s advisable to keep the input image under 5MB, between 2000×2000 and 640×480in resolution for a more accurate response. To avoid longer process times it´s recommended not to use higher resolution images. For best damage detection, it’s recommended to take a close picture of the area, approx. 80cm – 1.5m when taking the picture.
If the input is “draw_result”: true,
“image”: “https://i.insider.com/54da54336bb3f7156eeef89e?width=800&format=jpeg”, the result will look like this:
{ "draw_result": true, "job_id": "52af644d-03c9-46c8-ab8c-c486fc432ade", "output": { "elements": [ { "bbox": [ 372, 145, 489, 245 ], "damage_category": "slight_scratch", "damage_color": [ 50, 50, 100 ], "damage_id": "1", "damage_location": "rear_bumper", "score": 0.613146 }, { "bbox": [ 322, 411, 444, 481 ], "damage_category": "slight_scratch", "damage_color": [ 50, 50, 100 ], "damage_id": "1", "damage_location": "rear_bumper", "score": 0.602163 }