Aiming at the problem that the traditional model is poor in the multi-task detection of weld surface defects. For the first time, this study introduces a model based on the enhanced YOLO-V7 detection system to identify eight categories, which comprise seven defects categories and a good class of welding. This model addresses the existing poor accuracy of weld defects identification, and detection of fewer types of issues. Firstly, Wise-IOU is employed to swap out the original boundary frame loss function CIOU, and the quality of the anchor frame is evaluated using 'outlier' instead of the original IOU, and a wise technique for gradient gain allocation is provided to enhance the overall performance of the detector. Next, the Distributed Shift Convolution (DSConv) is introduced to replace the original module to form a new ELAN-D module, which achieves lower memory usage and higher computing speed. Then, the lightweight CARAFE upsampling operator is used to replace the original model upsampling operator. Finally, the detection head of YOLO-V7 is replaced with a decoupled head to make its detection head performance higher. The experimental findings demonstrate that the approach achieves an F1 factor of 87.0%, a precision of 91.8%, a recall of 87.7%, and a mAP of 91.7% on the weld defects datasets, and the combination of the above aspects shows that the current enhanced model outperforms other models.