基于双重检测的气门识别方法

针对目前工业中的气门识别方法存在重叠目标漏检率高、检测精度较低、目标包裹度差、圆心定位不准的问题,提出了一种基于双重检测的气门识别方法.首先,运用数据增强对样本进行轻量扩充;其次,以深度卷积网络为基础,加入空间金字塔池化层(SPP)和路径聚合网络(PAN),同时调整先验框,改进损失函数,从而提取气门预测框;最后,以霍夫...

Valve identification method based on double detection

Aiming at the problems that current valve identification methods in industry have high missed rate of overlapping targets， low detection precision， poor target encapsulation degree and inaccurate positioning of circle center， a valve identification method based on double detection was proposed. Firstly， data enhancement was used to expand the samples in a lightweight way. Then， Spatial Pyramid Pooling （SPP） and Path Aggregation Network （PAN） were added on the basis of deep convolutional network. At the same time， the anchor boxes were adjusted and the loss function was improved to extract the valve prediction boxes. Finally， the Circle Hough Transform （CHT） method was used to secondarily identify the valves in the prediction boxes to accurately identify the valve regions. The proposed method was compared with the original You Only Look Once （YOLO）v3， YOLOv4， and the traditional CHT methods， and the detection results were evaluated by jointly using precision， recall and coincidence degree. Experimental results show that the average precision and recall of the proposed method reaches 97.1% and 94.4% respectively， 2.9 percentage points and 1.8 percentage points higher than those of the original YOLOv3 method respectively. In addition， the proposed method improves the target encapsulation degree and location accuracy of target center. The proposed method has the Intersection Over Union （IOU） between the corrected frame and the real frame reached 0.95， which is 0.05 higher than that of the traditional CHT method. The proposed method improves the success rate of target capture while improving the accuracy of model identification， and has certain practical value in practical applications.