• Technical Name
  • 通曉運算量之AI模型架構優化、即時運算實現與資料集標註系統
  • Operator
  • National Chung Hsing University
  • Booth
  • AIoT&智慧應用 AIoT & Smart Applications
  • Contact
  • 詹淑琪
  • Email
  • shuchi@dragon.nchu.edu.tw
Technical Description \"1. CNN model architecture optimization techniques:Propose an Agilev3-L architecture, with a performance index of mAP@50:95FPS:FP16 up to 171.792. GOP-mode acceleration scheme for real time inference:employ GOP-modeTRACKING algorithms to enhance the processing rate. 3. Propose a rapid data labeling system HiTag\"
Scientific Breakthrough \"1. CNN model architecture optimization techniques:Propose a new model architecture Agilev3-L achieving a composite performance index of 171.79 (yolov3 is 135.24, yolov4 is 163.83)2. GOP based acceleration scheme for real time inference:Split the video input into I-framesP-frames. Only I-frames are predicted, while the P-frames results are obtained by tracking. For implementation of the Agilev3-L model on a Jetson Nano platform, the FPS is improved from the 3.91 to 27.09, indicating a 592 enhancement. AP@50 performance drops slightly from 85.21 to 84.69.\"
Industrial Applicability \"Systematic CNN modelarchitecture optimization techniques are developed to facilitate real time inference at edge sides. For the proposed Agilev3-L model, the implementation on a Jetson Nano platform can reach a processing rate of 27.09 FPS.We have also developed an auto-labeling system to expedite the tedioustime consuming ground truth bounding box labeling.\"