Track inspection and fault detection can be inefficient if it is done manually. My team therefore proposed a progressive web app (named E3) which can streamline and automate the overall process for engineers and technicians during track inspections. With actual LRT track faults images from the Land Transport Authority (LTA) and SBS transist, my team trained a machine learning model for fault detection, and hosted the model on the AWS cloud service which hosts the wep app backend as well. I am glad that my team was able to emerged winners for the hackathon organised by Garage@EEE and supported by Amazon and LTA!
