To address the issue of insufficient accuracy in defect segmentation caused by weak expression of surface defect characteristics on condenser copper tubes and feature confusion between similar defects, a feature-optimized method for surface defect segmentation on condenser copper tubes is proposed. Firstly, to address the problem of indistinct surface defects on condenser copper pipes, the method utilizes an attention optimization module based on the defect area attention enhancement strategy to enhance the feature expression ability of defects and suppress background feature expression. Secondly, through the use of dilation convolutions with varying rates and the integration of feature map optimization technology, cross-domain semantic capture of pixels is achieved and resolve the issue of feature confusion between similar defects. Finally, a multi-scale feature enhancement fusion method based on feature alignment is established to improve the model's detection ability for defects at different scales. Multiple sets of comparative experiments are conducted on images of condenser copper tubes which are captured in real production line environments, and the results show that the proposed method achieves the balance between the precision and the number of parameters when solving the above problems, and achieves a good segmentation effect. The algorithm achieves an average intersection over union of 80.53 % and a Dice coefficient of 88.94 %, with the model size being only 25 MB.