摘要： Laser stripe plays an important role in industrial vision measurement as the major auxiliary feature. Existing researches mainly focus on the application of small size parts. However, with the increase of field of view, it is difficult to extract laser stripes robustly in varying field measurement situations for the complex background, low proportion and uneven characteristic of laser stripes. To increase the measurement adaptability in complex environment, an automatic laser stripe detection and segmentation algorithm is proposed. First, the dataset is constructed by a large number of image patches collected in the field and laboratory, and laser stripe patches in the imbalanced dataset are expanded by data augmentation method. Next, the detection of the laser stripe is initially realized based on the training results of the convolutional neural network (CNN), and then the laser stripe is accurately detected by non-feature filtering criteria based on area constraints. Finally, a sub-regional feature clustering method is proposed to realize effective segmentation of uneven laser stripes. A large number of verification experiments have been carried out in both laboratory and field, and the results show that the proposed method can achieve automatic and accurate extraction of laser strips, which has strong adaptability to both the complex background in the field and the uneven brightness characteristic of laser stripes, satisfying the engineering requirements of large-scale parts field measurement.