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.

BeDIS has the following distinguishing features:1.Is easy to configure, deploymaintain 2.Location accuracyresponse time not affected by crowds 3.Can function without Internet 4.Supports waypoint-based indoor navigation 5.Supports diverse indoor location-based APPsservices

Based on deep learning camera/radar object detectiontracking technology, the proposed road side unit (RSU) system has achieved over 95 vehicle detection accuracy within 100m detection range in the processing performance of 10fps under nVidia Jetson Xavier platform. Compared to the 32-beam lidar based RSU, the proposed RSU achieves 97 reduction of sensor cost that exhibits high competitiveness in deployment cost. The proposed RSU system has been verified in fieldswe are now cooperating with an industry partner to deploy the RSU system in both TainanTao-Yuan cities.

The developed Unmanned autonomous underwater vehicles (AUV) included 3-D modeling technology, wireless power transfer between sub-system in AUV, GPS underwater locate system, low complexity frequency domain on ocean floor analyze with AI network, underwater objects detection network, image dehaze network on groundunder water, underwater color correction network, underwater objects classification network, underwater optical characteristic algorithmLED color compensation system.