Here are challenges that we have for you to discover cutting-edge prototype solutions.
Problem Statement 1:
Problem Statement Title: Real Time Monocular Depth Estimation on Edge AI
Eligible departments: Electronics & Communication, Electrical & Electronics, Instrumentation Engineering, Robotics, Artificial Intelligence – Machine Learning, Data Science, Data Analytics, Information Technology, Computer Science and Mechatronics only.
Problem Statement Description:
Monocular Depth Estimation is the task of predicting the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This depth information can be used to estimate the distance between the camera and the objects in the scene. Often, depth information is necessary for accurate 3D perception, Autonomous Driving, and Collision Mitigation Systems of Caterpillar vehicles. However, depth sensors are expensive and not always available on all vehicles. In some real-world scenarios, you may be constrained to a single camera. Open datasets like KITTI/NYUv2 can be used. Solutions are typically evaluated using Absolute Relative Distance Error metric. Based on the distance between the camera and the object (Cars/personnel), operator needed to be alerted visually using LED/Display/Audio warnings.
Expected solution & Tools that can be used:
Use either neural networks or classical algorithms on monocular camera images to estimate the depth. The depth estimation should be deployable on cheap edge AI devices like raspberrypi AI KIT (https://www.raspberrypi.com/products/ai-kit/) but not necessarily on raspberrypi.
Data Set/Reference Date KITTI Dataset can be used https://www.cvlibs.net/datasets/kitti/
Prototype requirements (If Applicable) Solution should be deployed in edge AI devices like
https://www.raspberrypi.com/products/ai-kit/ https://www.beagleboard.org/boards/beagley-ai https://www.ti.com/tool/SK-TDA4VM
but not limited to these Edge AI devices.
Problem Statement 2:
Problem Statement Title – Create Vision system to identify and display defects on components
Eligible departments: AI Data Science, Mechanical, Production, Manufacturing, Aeronautical, Industrial Engineering, Automobile, Metallurgy, Mining and Mechatronics Only
Problem Statement Description:
Visual inspection on Paint aesthetics, Weld aesthetics, gap measurements are currently manual posing issues on repeatability and dependence on manual processes. Technology solution needed to resolve the industry pain.
Expected solution:
Create an AI based Vision system with built in Deep Learning model to detect, identify and display defects on components which can qualify Paint appearance, Weld appearance, assembly quality and machining quality.
Problem Statement 3:
Problem Statement Title: DEF tank with mechanism to measure DEF quality & control DEF filling
Eligible departments: Mechanical, Production, Manufacturing, Aeronautical, Industrial Engineering, Automobile, Metallurgy, Mining and Mechatronics Only
Problem Statement Description:
An ideal DEF /AUS 32 solution used for NOX reduction should meet ISO 22241-1 standard and is typically 32.5% Urea in de-ionized water. However, many DEF solutions available in the mass market are either adulterated or not meeting this specification. This would impact tailpipe emissions, engine/machine operations & reduce the capability of after treatment systems. DEF systems are already equipped with the DEF quality sensor however the sensor would be able to sense only after the filling is completed when Key On. By this time, the tank would already be filled with adulterated urea or would adulterate the good DEF that’s already inside
Problem description and Expected solution:
Design and develop a DEF tank that would measure the quality of urea and equipped with a mechanism to control the filling of the DEF before it goes into the tank.
- The solution should indicate the machine operator regarding the condition of DEF quality before the tank is filled. The mechanism should be quick enough that the filling rate/time should have little of no impact.
- Design should be structurally durable to withstand harsh environmental conditions the DEF systems would be exposed to.
Constraints:
- Proposed design should be user friendly & capable of mass market production
- Understand & ensure the proposed solution should meet regulation requirements pertaining to Urea filling
- Standard filling gun specifications to be considered.
Expected Outcome:
- The proposed design solution to meet design/functional requirements, be cost effective, manufacturing & customer friendly
- Simulations to prove structural durability
- Working prototype that could demonstrate the expected capabilities