Monitoring the railroad’s components is crucial to maintaining the safety of railway operations. This article proposes a novel, compact computational vision system that works on edge devices, designed to provide precise, instantaneous assessments of rail tracks. This model reconfigures the teacher-student guidance system inherent in NanoDet by incorporating an innovative adaptively weighted loss (AWL) in the learning phase. The AWL assesses the caliber of the teacher and student models, establishes the weightage of the student's loss, and dynamically adjusts their loss contributions, directing the learning procedure towards effective knowledge transfer and direction. In comparison with cutting-edge models, our AWL-NanoDet boasts a minuscule model size of less than 2 MB and a computational expense of 1.52 G FLOPs, delivering a processing time of less than 14 ms per frame (evaluated on Nvidia’s AGX Orin). Compared to the original NanoDet, it also significantly enhances the model's accuracy by nearly 6.2%, facilitating extremely precise, instantaneous recognition of rail track elements.
How to Cite
Deep Learning, Rail inspection, Computer vision, Edge Computing
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