Presentation: Dive Deep into Building Computer Vision Systems for Self-flying Drones
Share this on:
Abstract
Every industrial drone in the world needs to see the world the way a pilot does, an obvious, but non-trivial feat of engineering. In this talk the challenge of building a computer vision/AI "sense and avoid" system is discussed, along with the challenges of a constrained cost/size/weight/power design environment. It’ll dive into deep learning training, simulation, and real-world aircraft testing with near-mid-air collisions, in order to keep these drones safe while they get integrated into the national airspace!
Tracks
-
Deep Learning Applications & Practices
Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.
-
Predictive Data Pipelines & Architectures
Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.
-
ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
-
Real-world Data Engineering
Showcasing DataEng tech and highlighting the strengths of each in real-world applications.
-
Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
The track where AI touches the physical world. AI use cases around drones, self-driving cars, ROS, NVidia Jetson, & Amazon Deep Lens.