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Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations
Article describes how the accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environment management. To address these challenges, the focus of the author's study is on detecting drainage crossings through the application of advanced convolutional neural networks.
Date:
November 12, 2023
Creator:
Zhang, Yicheng; Pandey, Dhroov; Wu, Di; Kundu, Turja; Li, Ruopu & Shu, Tong
System:
The UNT Digital Library
Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices
Article describes how embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images.
Date:
November 12, 2023
Creator:
Li, Yuke; Baik, Jiwon; Rahman, Md Marufi; Anagnostopoulos, Iraklis; Li, Ruopu & Shu, Tong
System:
The UNT Digital Library