An improved helmet detection method for YOLOv3 on an unbalanced dataset

An improved helmet detection method for YOLOv3 on an unbalanced dataset

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.

不平衡数据集上用于YOLOv3的改进头盔检测方法

YOLOv3目标检测算法由于其高速度和高精度而在工业中得到了广泛应用，但是它也存在一些局限性，例如不平衡数据集的精度下降。YOLOv3目标检测算法基于高斯模糊数据扩充方法来预处理数据集并改进YOLOv3目标检测算法。.. 通过有效的预处理，通常可以将YOLOv3的置信度提高0.01-0.02，而不会改变YOLOv3的识别速度，并且由于有效的特征融合，处理后的图像在图像定位中的表现也更好，这更加符合对识别速度和生产精度的要求。 （阅读更多）