1. CNN model architecture optimization techniques: Propose an Agilev3-L architecture, with a performance index of mAP@50:95FPS:FP16 up to 171.79
2. 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.

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.

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.

1.欲媒合之產業領域：資訊與通訊、電子與光電。2.欲媒合項目：技術合作、技術轉移。