A Workpiece-Dense Scene Object Detection Method Based on Improved YOLOv5

Abstract

Aiming at the problem of detection difficulties caused by the characteristics of high similarity and disorderly arrangement of workpieces in dense scenes of industrial production lines, this paper proposes a workpiece detection method based on improved YOLOv5, which embeds a coordinate attention mechanism in the feature extraction network to enhance the network’s focus on important features and enhance the model’s ability to pinpoint targets. The pooling structure of the space pyramid has been replaced, which reduces the amount of calculation and further improves the running speed. A weighted bidirectional feature pyramid is introduced in the feature fusion network to realize efficient bidirectional cross-scale connection and weighted feature fusion, and improve the detection ability of small targets and dense targets. The SIoU loss function is used to improve the training speed and further improve the detection performance of the model. The average accuracy of the improved model on the self-built artifact dataset is improved by 5% compared with the original model and the number of model parameters is 14.6MB, which is only 0.5MB higher than the original model. It is proved that the improved model has the characteristics of high detection accuracy, strong robustness and light weight.