To address the problems of poor detection flexibility, low yield rate and long detection time of smartphone glass cover defect detection methods, an improved YOLOv3 defect detection method for smartphone glass cover is proposed. A channel attention mechanism is added to the feature extraction network to solve the problem of inconspicuous defect features, a feature map of 104×104 dimensional size is added to the feature detection network to solve the problem of multi-scale defects, and finally the model is pruned to reduce the model parameters to improve the defect detection speed. The defect dataset is constructed by obtaining images covering four types of defects, such as chipped edge, pit, dirty and scratches, from the production site of smartphone glass cover. The proposed method and algorithms such as Faster R-CNN, YOLOv3 and YOLOv4 are compared for experiments and analysis. The experimental results show that the detection mAP (mean average precision) of the proposed method is 81.0% and the detection speed is 43.1 fps. Compared with the original YOLOv3 algorithm, the detection mAP is improved by 3% and the detection speed is increased by 6.7 fps. Compared with other deep learning algorithms, the detection speed and detection precision are improved. The proposed method meets the need for high-precision and efficient detection of defects in the industrial production site of smartphone glass covers.