The quality inspection after assembly of automotive interior parts is an important stage of assembly and an important guarantee for ensuring a high pass rate of interior parts assembly. The target detection hardware platform is built with low-power and high-performance NVIDIA development boards, and the Faster RCNN and YOLOv5 models are compared, and the YOLOv5 model, which has a better detection effect on small targets, is used to train the data collected by industrial cameras. The test results show that the accuracy of detecting 13 features of automobile interior fittings is as high as 95%, which realizes the efficient and accurate discrimination of automobile interior fittings and provides reliable auxiliary means for the assembly work of automobile interior fittings.