Computer vision technology has undergone significant transformation over the past decade. The field has shifted from traditional hand-designed algorithms toward AI-based solutions, driven by advances in neural network technology and the availability of large training datasets.
Algorithms
Neural networks have become increasingly dominant in vision applications. The adoption rate jumped dramatically: in 2016, roughly 38% of vision applications used neural networks, whereas in 2019, over 80% of vision applications used them.
Model consolidation represents another major trend. Rather than training models from scratch, developers increasingly leverage pre-trained models through:
- Direct out-of-the-box deployment
- Transfer learning with application-specific training data
- Feature extraction for downstream classification techniques
TensorFlow remains the leading framework for neural network development, though alternative frameworks are gaining traction.
Industry Trends
Edge deployment has become critical for AI vision systems. Computer vision at the edge is important from a performance, latency, bandwidth, privacy, reliability, and economic perspective.
Three-dimensional perception, utilising time-of-flight cameras and lidar systems for depth information, is experiencing rapid growth.
Hardware
Specialised chipsets optimised for cost and performance are proliferating. These devices can execute state-of-the-art neural networks at multiple frames per second while consuming minimal power.
While CPUs and GPUs currently dominate, the trend is shifting toward dedicated deep learning processors and digital signal processors.
Conclusion
AI-based vision sensing will remain a significant trend, with continued innovation driven by Industry 4.0. Running sophisticated artificial intelligence algorithms in embedded devices is now feasible and practical.
