Robust ELM-PID tracing control on autonomous mobile robot via transformer-based pavement crack segmentation

Abstract

Pavement crack tracing is paramount to missions encompassing automated crack sealing for road maintenance. However, existing methods still face several challenges, including the incapability to precisely extract crack trajectories and the challenge of tuning control parameters within intricate backgrounds. To address these limitations, the ViT-S2T network and the ELM-PID control system are proposed for crack tracing. Specifically, the ViT-S2T consists of two branches. The transformer-based feature extraction module (TFEM) integrates multi-head attention mechanism and multi-layer perceptron to capture global contextual crack semantic features. The incoherent segmentation masks (ISM) employs a binary classifier to predict the coarsest irrelevant mask and further performs upsampling fusion of higher-resolution features. Moreover, the Neural-PID control method is designed to track crack trajectories, combining Extreme Learning Machines (ELM) and Proportional Integral Derivative (PID). The ELM-PID controller utilizes a three-layer backpropagation neural network and proposes the ELM model for adaptive adjustment by predicting the tuning parameters of PID. This framework is applied to real-time visual tracing for edge AI. Extensive tests performed on three arduous datasets of DeepCrack, CFD, and S2TCrack, achieving a precision of 82.76% and mAP@0.5 of 75.63% and speed of 0.0513m/s, demonstrating the superior and robust nature of our approach in pavement crack tracing