The surface crack of the workpiece is similar to the background and the background is complex. In the detection process, problems such as missed detection, false detection, and difficulty in detection are prone to occur. To solve the above problems, this research proposes a workpiece surface crack detection technique based on morphology and improved YOLOv5. To improve the ability of the model to extract global information, the erosion method in morphology is used to improve the crack feature of the data set. Next, the global and local information in the YOLOv5 backbone network is fused by MobileViTv3. And finally, SIoU is used as the loss function of the bounding box regression to improve the accuracy of the bounding box localization. After comparative experiment verification, the designed model achieves 73.1% and 74.6%accuracy on the original and corrosion datasets, respectively. Accuracy of the model improves by 13.6% and 15.1%, respectively. The results of the example experiment show that the method proposed in this paper has a good detection effect, realizes the accurate detection of cracks on the device surface. And it provides a novel idea for using deep learning to detect cracks in real scenes.