基于RetinaNet和类别平衡采样方法的销钉缺陷检测

Defect detection of pins based on RetinaNet and class balanced sampling methods

传统的无人机巡检航拍图中的电力连接金具销钉缺陷检测依赖人工进行标注,针对此问题,借助深度学习缺陷检测算法RetinaNet自动提取正常、缺陷样本的特征,完成低层特征和顶层特征的融合,实现销钉缺陷的自动标注。考虑到现实情况中缺陷类别样本数量远少于正常类别样本数量,首先分析了缺陷数据不足引起的类别失衡对识别结果的影响,结果表明该情况下训练好的模型将会使得大量缺陷样本被错误地识别为正常类。于是,在数据层面采用类别平衡采样方法,确保每个类别参与训练的机会均衡,实验结果表明,所提的方法能够在维持销钉正常类的高识别率前提下,明显提高缺陷类别的平均准确率。

The traditional detection method for the defects of the pin on the power connection fitting in the aerial survey of the drone is dependent on manual marking. Aiming at this problem, the deep learning algorithm RetinaNet is used to automatically extract the features of normal and defective samples and complete the fusion of low-level features and top-level features to achieve automatic labeling of defects. Considering the fact that the number of defective samples is much smaller than the normal number of samples, Firstly, the influence of the category imbalance caused by the deficiency of the defect sample on the recognition result is analyzed. The results show that the trained model in this case will make a large number of defective samples be mistakenly recognized as normal classes. Therefore, at the data level of RetinaNet, class balanced sampling is proposed to ensure that each category has the same opportunity to participate in training. The experimental results show that the proposed method can significantly improve the average precision of defect categories under the premise of maintaining the high recognition rate of normal categories.