In view of the fact that the current detection technology for the end face defects of cigarette sets cannot accurately identify specific defect types, does not take into account the limitations of identifying filter nozzle deformation or smoke foam inclusion, and has insufficient detection accuracy, the work aims to introduce a detection strategy based on lightweight YOLOv7 model. An image acquisition system was designed and installed on the GDX2 packaging machine, and the YOLOv7 architecture was customized by integrating lightweight network structures ShuffleNetV2, DWConv and DSConv, which effectively reduced the number of model parameters and accelerated the training and reasoning process. Combined with the SimAM attention mechanism, the model's ability to pay attention to minor defect features was further enhanced, and the accuracy of minor appearance defect recognition was significantly improved. The experimental results showed that the number of parameters and calculation amount of the proposed algorithm decreased by 85% and 81%, and the value of mAP@0.5 reached 0.973. The average accuracy of filter deformation and smoke inclusion among defect types reached 0.978 and 0.945, respectively. At low computing power (11.15 TFLOPS), the system's reasoning time was as low as 1.98 ms. This technology has been applied to the detection of end face defects of GDX2 cigarette packaging machine, which can effectively improve the comprehensive performance of defect detection and provide an efficient and economical appearance quality control solution for the tobacco industry.