Improved YOLOv5 Longan Fruit Stalk Position Recognition and Detection Method Based on YOLOv5
A longan fruit stalk recognition method based on the improved YOLOv5s target detection algorithm was proposed in order to meet the demand for high effect stalk position recognition and refined management of processing operations.Aiming at the influence factors such as the small size of longan and the similarity between the stalk and the colour of the fruit,the CBAM attention mechanism is introduced to strengthen the attention learning of the stalk location features and weaken the influence of the rest of the information on the recognition results.At the same time,the model complexity is simplified to reduce the detection time.The weighted bidirectional feature pyramid network BIFPN is used in the neck network to optimise the multi-scale feature fusion mechanism and improve the model detection accuracy.The experimental results show that the improved YOLOv5 algorithm detects and recognises better than the initial model,and can meet the requirements for the precision and efficiency of fruit stalk position detection in automated longan shelling.
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