摘要
Object detection plays an important role in the sorting process of mechanical fasteners.Although object detection has been studied for many years,it has always been an industrial problem.Edge-based model matching is only suitable for a small range of illumination changes,and the matching accuracy is low.The optical flow method and the difference method are sensitive to noise and light,and camshift tracking is less effective in complex backgrounds.In this paper,an improved target detection method based on YOLOv3-tiny is proposed.The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression(NMS)instead of the hard decision NMS algorithm.This not only increases the size of the network structure by 52×52 and improves the detection accuracy of small targets but also uses the basic structure block MobileNetv2 in the feature extraction network,which enhances the feature extraction ability with the increased network layer and improves network performance.The experimental results show that the improved YOLOv3-tiny target detection algorithm improves the detection ability of bolts,nuts,screws and gaskets.The accuracy of a single type has been improved,which shows that the network greatly enhances the ability to learn objects with slightly complex features.The detection result of single shape features is slightly improved,which is higher than the recognition accuracy of other types.The average accuracy is increased from 0.813 to 0.839,an increase of two percentage points.The recall rate is increased from 0.804 to 0.821.
基金
The authors gratefully acknowledge the support provided by the National Natural Science Foundation of China(No.U20A20265)。
