Recently， wire harnesses are widely used. The harness terminal， an important component of a harness， requires strict quality inspection. Therefore， to improve the accuracy and efficiency of harness terminal quality detection， a real-time semantic segmentation network using multiple receptive field （MRF） attention， called MRF-UNet， is proposed in this study. First， an MRF attention module is used as the basic module for network feature extraction， improving the feature extraction and generalization abilities of the model. Second， feature fusion is used to effect jump connections and reduce the computational load of the model. Finally， deconvolution and convolution are used for feature decoding to reduce the network depth and improve the algorithm's performance. The experimental results demonstrate that the mean intersection over union， mean pixel accuracy and dice coefficient of the MRF-UNet algorithm on the harness terminal test dataset are 97.54%， 98.83%， and 98.31%， respectively， and the reasoning speed of the model is 15 FPS. Compared with BiSeNet， UNet， SegNet， and other mainstream segmentation networks， the proposed MRF-UNet network exhibits more accurate and faster segmentation results for microscopic images of harness terminals， thus providing data support for the subsequent quality detection.