The rods produced by the high-speed assembly line are highly susceptible to various surface defect, but the defect identification method based on conventional image processing is unreliable and susceptible to environmental factors, while the defect identification method based on deep learning suffers from oversized models and recognition accuracy that is constrained by the quantity of samples. Therefore, this paper suggests an identification system of rods surface defect based on improved SqueezeNet. An acquisition device was designed to obtain the full surface image of the circumferential rods, and the attention module is introduced into the lightweight convolutional neural network SqueezeNet to improve the feature extraction effect of the model, data balancing is used to improve the recognition accuracy of minority samples, transfer learning is employed for deep learning training to minimize the impact of insufficient samples on the training effect. Taking the cigarette on the production line as the research object, the circumferential surface image of the cigarette is collected for experiment, the results show that the classification accuracy of the improved method under the condition of few samples reaches 94. 49%, with the F1 score for minority samples being improved by 31. 19%, and the detection time of a single image being approximately 1. 66 ms. Additionally, the model is lightweight, meeting the need for rods in industrial production lines to have real-time defects recognized.