"Optimizing Image Quality and Stereo Depth at the Edge," a Presentation from John Deere
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/optimizing-image-quality-and-stereo-depth-at-the-edge-a-presentation-from-john-deere/ Travis Davis, Delivery Manager in the Automation and Autonomy Core, and Tarik Loukili, Technical Lead for Automation and Autonomy Applications, both of John Deere, present the “Reinventing Smart Cities with Computer Vision” tutorial at the May 2023 Embedded Vision Summit. John Deere uses machine learning and computer vision (including stereo vision) for challenging outdoor applications such as obstacle detection, vision-based guidance and weed management, among many others. The quality of the images the company’s systems obtain, and the accuracy of the depth information produced by its stereo cameras, significantly impact the performance of the overall solutions. In this talk, Davis and Loukili share some of the challenges John Deere has faced in developing image quality improvement and stereo vision algorithms. Many of the techniques found in academic research and prior work cannot be easily implemented in real-time applications at the edge and are difficult to scale for applications with varying performance and cost requirements. They highlight some of the alternative techniques their company has developed to provide optimized image-quality and stereo vision implementations that meet the requirements of John Deere’s product range. Stated another way, they share how they do more with less.
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Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
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Lokwon Kim, CEO of DEEPX, presents the “Toward the Era of AI Everywhere” tutorial at the May 2023 Embedded Vision Summit.
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You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
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Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
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Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit.
As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias.
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Baharak Soltanian, Vice President of Research and Development for the Sanborn Map Company, presents the “Sensor Fusion Techniques for Accurate Perception of Objects in the Environment” tutorial at the May 2023 Embedded Vision Summit.
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- 1. Doing More with Less: Optimizing Image Quality and Stereo Depth at the Edge Tarik Loukili & Travis Davis Automation & Autonomy John Deere
- 2. Overview • John Deere Background • Image Quality Considerations • Stereo Method Comparisons • Q&A © 2023 John Deere 2
- 3. Technology at John Deere 5 © 2023 John Deere
- 4. Low Light Color Temp Exposure Control Stuck Material Dust Flare/ Glare Motion Blur Image Quality Noise [1] [2] © 2023 John Deere 6 What Makes A Good Image For Computer Vision & Machine Learning?
- 5. • 120 ft boom • 96 nozzles • 34 cameras • 5 controllers • @ 12.5 mph Configuration © 2023 John Deere 7 See & Spray Select – Use Case
- 6. 8 vs Captured Expected 𝒎𝟏 𝒎𝟐 𝒎𝟑 𝒎𝟒 𝒎𝟓 𝒎𝟔 𝒎𝟕 𝒎𝟖 𝒎𝟗 + 𝒄𝟏 𝒄𝟐 𝒄𝟑 CCM White Balance Bayer Interpolation CCM Gamma Color Conversion © 2023 John Deere Color Correction
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- 8. © 2023 John Deere 10 [4] Stuck Material On The Lens • Stuck Material maybe hard to detect especially when dealing with translucent material. • Not all stuck material necessarily impact the image processing.
- 9. Stuck Material On The Lens © 2023 John Deere 11 • Computing a Stuck Material mask Make it easier to estimate the impact on the viewing ROI • The impact may be different based on application
- 10. © 2023 John Deere 12 Atlas Algolux [6] Visionary.AI [5] Dealing With Shadows • Tunning the ISP settings may be challenging or impossible to get the optimum setting to deal with issues such as shadows.
- 11. Left Image Right Image Disparity Stereo Vision © 2023 John Deere 13
- 12. (274, 133) (274, 133) (242, 133) (224, 133) Left Image Right Image Disparity: dx = xl-xr = 274-242 = 32 Distance: D = fB/dx = (focal length x baseline) / disparity [7] © 2023 John Deere 14 Stereo Correspondence
- 13. [7] © 2023 John Deere 15 Stereo Disparity Image
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- 15. OpenCV StereoBM [8] Further Away Closer Invalid (Dark Blue) Block Matching Example – Minimal Filtering © 2023 John Deere 17
- 16. OpenCV StereoSGBM [9] Further Away Closer Invalid (Dark Blue) © 2023 John Deere 18 Semi-Global Block Matching
- 17. iRaftStereo_RVC [10] Further Away Closer © 2023 John Deere 19 Neural Net Based Stereo Approach (Machine Learning – RAFT)
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- 19. Block Matching AI Matching (RAFT) Pixel by Pixel Disparity Differences Disparity Differences > 2 Reference Image 21 Stereo Method Differences
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- 22. Image Quality References: 1. https://www.adobe.com/uk/creativecloud/photography/discover/color-changer.html 2. https://www.photoreview.com.au/tips/shooting/iso-and-image-quality/ 3. ttps://www.lumistrips.com/lumistrips-blog/color-temperature-explained/ 4. www.lensrentals.com 5. https://algolux.com/newsroom/algolux-brings-atlas-to-the-cloud-to-democratize-camera-isp- optimization-for-computer-vision/ 6. https://www.visionary.ai/ Stereo References: 7. Stereo Reference Example Image from https://vision.middlebury.edu/stereo/data/scenes2003/ 8. OpenCV Stereo Block Matching from opencv/stereobm.cpp 9. OpenCV Semi Global Block Matching from opencv/stereosgbm.cpp 10. RAFT Stereo from princeton-vl/RAFT-Stereo Special “Thanks” for Content & Contributions Zach Bonefas, Nick Butts, Ruveen Perera, & Vincenzo Macri © 2023 John Deere 24 References