To simplify the tedious and inefficient manual inspection for connector surface cracks, an intelligent inspection method for connector surface cracks based on machine vision is proposed. To find the region to be examined and the border, the lower boundary of the area to be inspected is first fitted using a fitting approach based on random sampling consistency. Among them, the morphological operation method based on single scale can not effectively extract the crack region. Based on the crack characteristics, this study proposes a crack extraction technique based on multiscale morphological operation, and the comparative experiment demonstrates that this method has a good result and accomplishes the coarse extraction for the fracture region. Then, according to the crack structure characteristics, an adaptive threshold segmentation method is proposed to complete the segmentation for the crack region. Finally, Blob analysis is used to statistically distinguish between genuine and false cracks based on the geographical information of the target connectivity domain and the gray-scale response intensity of the target area. Results show that the proposed method achieves real-time online detection for connector surface fractures with a detection accuracy of 97.1%.