Abstract
Combining an object-detection algorithm with an unmanned aerial vehicle (UAV) can accelerate the detection of road cracks. To address the difficulties of intricate crack morphology, similar color to the road, and small crack area, this paper describes a UAV road crack object-detection algorithm using MUENet. The MUENet is primarily comprised of a main and auxiliary dual-path module (MADPM), an uneven fusion structure with transpose and inception convolutions (TI-UFS) and a E-SimOTA strategy. First, the MADPM is proposed to efficiently extract the essential morphological features of cracks. Subsequently, the TI-UFS is proposed to explore potential crack color characteristics. Finally, the E-SimOTA strategy accurately differentiates different types of cracks and accelerates network training convergence. The experimental results demonstrate that MUENet has the double benefits of precision and speed on a self-built dataset of UAV near-far scene images (UNFSI). This object-detection algorithm is more adaptable to crack objects than other mainstream object-detection algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
