The YOLOv3 target detection algorithm is widely used in industry due to its high speed and high accuracy, but it has some limitations, such as the accuracy degradation of unbalanced datasets. The YOLOv3 target detection algorithm is based on a Gaussian fuzzy data augmentation approach to pre-process the data set and improve the YOLOv3 target detection algorithm. Through the efficient pre-processing, the confidence level of YOLOv3 is generally improved by 0.01-0.02 without changing the recognition speed of YOLOv3, and the processed images also perform better in image localization due to effective feature fusion, which is more in line with the requirement of recognition speed and accuracy in production.

翻译：YOLOv3目标探测算法由于速度快、精确度高,在工业中广泛使用,但有一些局限性,例如不平衡数据集的准确性降低;YOLOv3目标探测算法基于高斯模糊数据增强法,用于预处理数据集和改进YOLOv3目标检测算法;通过高效预处理,YOLOv3的可信度一般通过0.01-0.02得到提高,而不会改变YOLOv3的识别速度;由于有效的特征聚合,经过处理的图像在图像定位方面也表现得更好,这更符合在制作过程中的识别速度和准确性要求。