Abstract: In response to the high cost, slow speed, and difficulty in deploying deep learning models in weld defect detection, this paper proposes a lightweight and high precision optimized detection model (LHODM) with good performance. Firstly, a new backbone network is built and an ultra lightweight convolutional ULConv is designed as the network foundation convolution to reduce model depth, and Depthwise operation was used to generate more effective redundant feature mappings, reducing the amount of parameters and computation. Secondly, an efficient and lightweight dedicated module ELCC is designed. Under the premise of lightweight response model, the distribution characteristics of weld defects and imaging rules are considered, and the relationship between isolated areas is captured from both horizontal and vertical directions.Combined with the lightweight up-sampling operator CARAFE, the model has a larger receptive field during feature recombination, makes more effective use of ambient information, and makes up for the loss of lightweight accuracy. Finally, in order to improve convergence speed and loss function efficiency, an OS-IoU loss function is designed, taking into account the angle between the expected regression vectors, redefining the penalty term and correlation, and strengthening the attention to distance loss and shape loss. The results showed that the LHODM model achieved detection accuracy and speed of 91.62% and 63.47 frames per second, with a model memory cost of only 3.99 million, effectively solving the problems of high cost and slow speed in weld defect detection work.