The defect detection of automotive assembly parts is an important part in the automotive manufacturing process, which can not only improve product quality, reduce the return rate, avoid cost waste, but also provide safety protection for drivers. The earliest defect detection relies on expert experience, which is low accuracy and high labor cost. The nondestructive testing technology relies on media and is not efficient. The introduction of machine vision can not only balance the problem of detection accuracy and efficiency, but also improve the robustness of the detection system, which is one of the most promising defect detection technologies. This article firstly gives the definition and main process of visual defect detection, briefly introduces the hardware of image acquisition in visual defect detection system. Then, the research progress of automobile assembly defect detection in recent years is reviewed from three aspects of commonly used defect segmentation methods, feature extraction methods and convolutional neural networks. The advantages and disadvantages of related methods are compared and analyzed. The automobile assembly parts are roughly divided into four categories, such as wheel tires, body paint, parts and engines. The research status of defect types and defect detection algorithms are summarized. Next, 10 data sets related to the automobile industry and defect detection performance evaluation indicators are introduced. Finally, it is pointed out that the defect detection of automobile assembly is faced with many technical challenges, and the prospect of further work is given.