This technology integrates 1000+ times of UAV imaging experiences with labeled rice lodging images for training. A rice lodging recognition model using deep learning reaches 90 accuracy. The recognition model can be deployed in a microcomputer mounted on UAVs to implement edge computing. While taking aerial images, the inference can be completedreveal lodging areadamage level in-time.

We provides a wearable device for the visually impaired to walk outdoors. By the deep learning network, the system can recognizeguide the visually impaired to walk on safe areas such as sidewalkscrosswalk. In addition, it can recognize the types of common obstaclesguide the visually impaired to avoid it in advance. Finally, we can convert the Google Maps route into easy-to-understand voice prompts instruction to guide the visually impaired to move in the right direction.

Low-power deep learning accelerator integrates neural network design / training, model compressionaccelerator design. It uses a small amount of computationmemory footprint to realize high performance on the edge device. The image semantic segmentation technology can reach 80 frames per second (resolution:1024*2048) to meet real-time requirements for autonomous driving.

This invention presents a "next-generation augmented reality navigation system” using the generative adversarial network-long short term memory network (GAN-LSTM) framework with integrated GPS module to implement a novel AR navigation system. Unlike the presented AR navigation system, the virtual guided path is "autonomously generated" in captured image rather than superimpose on the image by using the pre-rendered 3D content, which not only provide a more authenticcorrect AR effect to user but also earlycorrectly guide the driver when driving in complex road traffic environment.