With the rapid development of rail transit, the detection of track defects has become crucial for ensuring safety. This paper systematically reviews common types of track defects, such as fatigue cracks, burns of rails, and fastener looseness. It elaborates in detail on detection technologies including ultrasonic, eddy current, magnetic flux leakage, and machine vision, as well as their principles, applications, and advancements. This encompasses various derivative methods of ultrasonic detection, such as conventional ultrasound, phased array ultrasound, laser ultrasound, and ultrasonic guided waves. Additionally, it covers the innovations in eddy current detection regarding the suppression of the skin effect and combination with thermal imaging; the improvements in magnetic flux leakage detection in terms of signal processing and new lift-off layer design; and the characteristics of traditional image processing and deep learning methods in machine vision detection. Meanwhile, the application achievements of multi-source information fusion technology in track defect detection are expounded. For example, defect identification and localization are realized by collecting data from multiple technologies and integrating deep learning models. Finally, the challenges faced by multi-source technology fusion are analyzed, and suggestions for future research directions are proposed, providing a comprehensive reference for the development of track defect detection technologies.