Optimizing Camera Image Quality to Maximize Computer Vision Results

Applications of computer vision have broadly expanded thanks to deep learning, which achieves much better results than classical techniques. This is evident in our cell phone apps; video security, IoT and smart city solutions; and in cars and autonomous vehicles. Safety-critical applications especially need robust accuracy. Unfortunately, as seen in recent AAA reports, widely reported failures of Tesla automatic braking, and reports from system developers, there are significant, disheartening gaps in the effectiveness of the latest systems when deployed in diverse real-world conditions. In this talk, Algolux will present proven breakthrough approaches that address the limitations of current camera design and ISP tuning methodologies and result in significantly improved computer vision performance. We’ll illustrate the effectiveness of these techniques with examples from real-world road scenarios using current automotive vision system architectures, and we’ll introduce new vision system architectures that provide even more robust detection and depth perception.