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    • Personalized emotion sensing for spoken dialog interface

      AI & IOT Application 未來科技展 Personalized emotion sensing for spoken dialog interface

      The technology is an integrated solution incorporating the retrievable personality for multimodal emotion sensing for spoken services. This framework provides a real-world flexibility that enables the estimation of the target speaker emotion states without manual personalization.
    • Occlusion resistant face detectionrecognition system

      AI & IOT Application 創新發明館 Occlusion resistant face detectionrecognition system

      The system, “Occluded Resistant Face DetectionRecognition System”, contains different scale detectors for calculating face locations. Additive angular margin loss is added into the training phase for achieving high efficiencyaccuracy. The system can achieves 80 accuracy under the 50 face occluded.
    • Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      AI & IOT Application 未來科技展 Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      This study built an automatic insect pest image identification system based on tiny Yolov3 deep learning model. By optimizing the tiny Yolov3 detection model, images of insect pests on scanned sticky paper can be automatically identified. The system achieves a testing accuracy of 0.93, 0.90 for whitefliesthrips respectively.
    • Extreme small object identification for UAV monitoring

      AI & IOT Application 創新發明館 Extreme small object identification for UAV monitoring

      We develop the new neural network architecture to improve execution speedaccuracy. The result shows that our AI detection system can run on a microcomputer in real-time, meanwhile its accuracy 90. We put that microcomputer on a dronereturn the result to smartphone App in real-time.
    • Real-time identification of crop losses using UAV imagery

      AI & IOT Application 未來科技展 Real-time identification of crop losses using UAV imagery

      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.
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