During the detection phase of computer vision in industry, insufficient data occurs frequently. In this scenario, data augmentation can effectively resolve the issue and improve the performance of the model. Presently, image data augmentation has become a standard model configuration, but few image data augmentation approaches are available for industrial object detection. This paper introduces a new method for augmenting picture data, Random Interpolation Resize (RIR), which can enhance the detection effect of industrial data sets without the requirement for extra resources. The resize operation is a standard configuration in the preprocessing stage, so we alter the interpolation method from fixed collocation to randomly selected interpolation to increase the amount of image data, enhance the model's generalizability, and enhance its detecting capabilities. Multiple data sets were utilised in experiments. The mAP of the YOLOv5s model on the GC 10 dataset increased by 4.8%, the mAP of the Yolov5n model on the Neo-DET dataset increased by 1.4%, and the mAP of the YOLOv5s model on the battery steel shell dataset climbed by 0.9%. The mAP of the YOLOv5 model was increased by 0.5% when applied to the VOC dataset.