The work aims to improve the detection accuracy and detection speed of small defects on surface of workpieces on the automated production line. First of all, the use of CutMix data enhancement method in the preprocessing stage was proposed to increase the diversity of training samples, improve the robustness and generalization ability of the model, and avoid overfitting of the training model. K-means++ clustering algorithm was used to generate boundary candidate boxes to adapt to defects of different sizes and to screen out finer features earlier. Secondly, the CSP Darknet53 network and SPP module were used to extract the features of the input original image, and obtain an online detection model for the surface quality of the workpiece through training, so as to improve the accuracy of YOLOV4 defect location detection and recognition. The experimental results showed that the online monitoring method of workpiece surface quality based on YOLOV4 proposed in this work had a prediction accuracy of 97.5% and a detection speed of 32.8 FPS, which were superior to similar deep learning algorithms. The automated production line of an aviation industrial product in Guizhou was used as an experimental platform to verify the feasibility and effectiveness of the proposed method. Experimental results show that the method has the advantages of simple and clear structure, strong adaptability, etc. The detection accuracy and speed meet the needs of industrial scenarios, and it can be used for online detection of product surface quality.