Abstract
To solve the problems of similar product shapes, the low recognition rate of scattered workpieces and poor real-time performance in industrial sorting scenarios, a workpieces target detection algorithm based on improved YOLOv5 was proposed. The algorithm replaced the characteristic b network with a lighter ShuffleNetV2 network to achieve a lightweight network model, balance speed and accuracy. BiFPN structure was introduced to change the neck and enhance the ability of feature extraction at different scales. The research results indicate that the proposed improved algorithm has more advantages in industrial scenarios. It reduces the identification error rate of the target workpiece in the case of scattered and meets the requirements of accuracy and real-time recognition of the workpiece.
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