The improper crimping height of the wire harness terminal leads to the wire core being cut off or having a large gap. In manual detecting number of wire cores and judging whether the wire core is broken after terminal crimping, there are many challenges, such as high labor intensity and visual fatigue. In this paper, a wire core detection method based on deep learning in the microscopic image of the wire harness terminal for counting is proposed. K-means multidimensional clustering algorithm was used to cluster the wire core bounding boxes to generate the anchor boxes that conform to the distribution of the core bounding boxes, aiming at the properties of dense and irregular arrangement of terminal wire core microimaging. The gradient equalization mechanism was used to reconstruct the loss function to deal with the extremely uneven category of anchor boxes with different attributes in the terminal image. The results show that the algorithm proposed in this paper achieves a mean average precision of 96.2% while maintaining the real-time performance and the same counting accuracy as the manual, compared with the other object detection algorithms. The proposed algorithm can be used for wire core counting and crimping quality evaluation of wire harness terminals.