论文标题
Yolov3在不平衡数据集上的改进的头盔检测方法
An improved helmet detection method for YOLOv3 on an unbalanced dataset
论文作者
论文摘要
Yolov3目标检测算法由于其高速和高精度而广泛用于行业，但它具有一些局限性，例如不平衡数据集的准确性降低。 Yolov3目标检测算法基于高斯模糊数据增强方法，以预处理数据集并改善Yolov3目标检测算法。通过有效的预处理，Yolov3的置信度通常在不改变Yolov3的识别速度的情况下提高0.01-0.02，并且由于有效的特征融合，处理后的图像在图像定位方面的性能也更好，这更符合识别速度和生产中识别速度和准确性的要求。
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.
