A study on low-cost, high-accuracy, and real-time stereo vision algorithms for UAV power line inspection

Hongyu Wang; Baomin Zhang; Xun Zhao; Cong Li; Cunyue Lu

doi:10.1117/12.2309434

13 April 2018 A study on low-cost, high-accuracy, and real-time stereo vision algorithms for UAV power line inspection

Hongyu Wang, Baomin Zhang, Xun Zhao, Cong Li, Cunyue Lu

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961L (2018) https://doi.org/10.1117/12.2309434
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

Conventional stereo vision algorithms suffer from high levels of hardware resource utilization due to algorithm complexity, or poor levels of accuracy caused by inadequacies in the matching algorithm. To address these issues, we have proposed a stereo range-finding technique that produces an excellent balance between cost, matching accuracy and real-time performance, for power line inspection using UAV. This was achieved through the introduction of a special image preprocessing algorithm and a weighted local stereo matching algorithm, as well as the design of a corresponding hardware architecture. Stereo vision systems based on this technique have a lower level of resource usage and also a higher level of matching accuracy following hardware acceleration. To validate the effectiveness of our technique, a stereo vision system based on our improved algorithms were implemented using the Spartan 6 FPGA. In comparative experiments, it was shown that the system using the improved algorithms outperformed the system based on the unimproved algorithms, in terms of resource utilization and matching accuracy. In particular, Block RAM usage was reduced by 19%, and the improved system was also able to output range-finding data in real time.

Citation Download Citation

Hongyu Wang, Baomin Zhang, Xun Zhao, Cong Li, and Cunyue Lu "A study on low-cost, high-accuracy, and real-time stereo vision algorithms for UAV power line inspection", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961L (13 April 2018); https://doi.org/10.1117/12.2309434

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