改进YOLOv4的表面缺陷检测算法

为解决航空发动机部件表面缺陷检测精度低、检测速度慢的问题,提出一种改进的YOLOv4算法进行智能检测。在路径聚合网络（PANet）结构中融合浅层特征与深层特征，增大特征检测尺度，同时去除自下而上的路径增强结构，提高小目标检测精度和整体检测速度；根据各类缺陷数量不同的情况，优化聚焦损失中的平衡参数，增加权重因子调节各类缺陷的损失权重，将改进后的聚焦损失代替分类误差中的交叉熵损失函数，降低样本不平衡和难易样本对检测精度的影响。实验表明：相比于原始YOLOv4算法，改进后的YOLOv4算法在测试集上的平均精度均值（mAP）为90.10%，提高了2.17%；检测速度为24.82 fps，提高了1.58 fps，检测精度也高于单发多框检测（SSD）算法、EfficientDet算法、YOLOv3算法和YOLOv4-Tiny算法。

Surface defect detection algorithm based on improved YOLOv4

In order to enhance the accuracy and speed of surface defect detection of aeroengine components, an improved YOLOv4 algorithm is proposed for intelligent detection. Firstly, shallow features and deep features were integrated into the path aggregation network (PANet) to improve the feature detection scale, and the bottom-up path augmentation structure was removed to increase the accuracy of small target detection and the overall detection speed. Then, according to the numbers of various defects, the balance parameter of the focal loss was optimized, and a weight factor was added to adjust the loss weight of various defects. The improved focal loss was used to replace the cross-entropy loss function in the classification error, thus reducing the impact of imbalanced samples and hard and easy samples on the detection accuracy. The experimental results show that the mean average precision (mAP) of the improved YOLOv4 on the test set is 90.10%, which is 2.17% higher than that of the traditional YOLOv4, and the detection speed is 24.82 fps, which is increased by 1.58 fps. The detection accuracy is also higher than other algorithms including single shot multibox detector (SSD), EfficientDet, YOLOv3 and YOLOv4-Tiny.