Aiming at the current market mainstream weldment surface defects model detection accuracy is not high, the model is complex and does not meet the real-time monitoring and other issues, a new detection model of weldment surface defects based on the improvement of YOLOV7-tiny obtained KThin-YOLOV7 is proposed.Firstly, the EMA-BasicRFBC module, which is based on simulating the human visual sensory field, was designed to replace the spatial feature pyramid SPP module of the YOLOV7-tiny model, so as to enhance the performance of the model feature expression.Secondly, the ThinNeck structure is designed based on the SlimNeck design paradigm structure, and it is used to replace the NECK feature fusion part of YOLOV7-tiny, which reduces the number of parameters and computation of the model and improves the average detection accuracy of the model at the same time. Finally, the K-means++algorithm is introduced to find out the appropriate anchor frame and replace the LOSS of the original model with the FEIOU loss function, which further helps the model to optimize the position and size of the target frame.The mAP of the KThin-YOLOV7 is improved by 7.11% to 87.64% compared to the original YOLOV7-tiny model, while the number of parameters and computation of the model are decreased by 11.14% and 15.5%, respectively. decreased by 11.14% and 15.26%, respectively. Experimental results show that KThin-YOLOV7 can efficiently and accurately locate and detect defects on the surface of welded parts.