This project will develop a unmanned aerial vehicle (UAV)-imagery based intelligent track component health condition inspection system, that will utilize a camera and GPS in a UAV integrated with edge computer device to identify missing and broken fasteners at the real-time speed. There are a considerable number of studies on the use of UAVs in track inspection. However, these studies utilize drones as a carrier of cameras and need human pilots to operate and control the drones. The collected images are stored onboard for a later analysis at some centralized facility. So, the current practices based on these studies has several drawbacks and limitations. Human pilot cost can be significant. Images collected by different pilots at the same track segment could vary and depend on the pilots’ operation skill, experience, and judgement. The inspection route is also subjective. The delay between data collection, data processing, and decision making depreciate the value of the inspections because track conditions can quickly deteriorate as traffic accumulates. To address these limitations, this project proposes a next generation UAV-imagery based track inspection system featuring advanced computer vision for real-time fattener defect defection and  efficient edge computing for field data processing. The advanced, embedded computer vision model will extract the features of various track components to evaluate their health conditions, such as missing or broken spikes, clips, rail surface detect, welding crack, broken ties. All inspected data will be immediately processed onboard for track condition assessment without the need for intensive data storage or transferring. The processed results will also be linked to the image-acquisition locations with the on-board GPS unit of the UAV. Both software and hardware are based on a modular and open-source design, which makes it compatible and transferable to other drone platforms, which to the best of the proposer’s knowledge is still unavailable in commercial or academic sectors. The proposed research consists of three modules: Module I --Training Image Library module; Module II -- AI-based Track Component Detection module; and Module III -- Edge-computing system module. . Module I will establish specialized drone-based track image database for convolutional neural network (CNN)-based computer vision model training. In Module II, a pixel-level detection system will be developed by using a tailored instance segmentation model to detect track components in a fast and accurate fashion. In Module III, to enable in-situ image analysis and AI inference, an appropriate mobile edge-computing platform and integration strategy will be developed. The proposed system will significantly reduce inspection cost and derailment risk, optimize maintenance strategy, and improve track safety. It will also greatly reduce the workload and improve the work conditions for the track inspectors because the system will automatically process the images and record the detected defects and access the track where it is hard to reach for the inspectors.