With the development of modern agricultural technology,the automation of strawberry production and picking is an inevitable trend,and strawberry object detection is a key link to achieve picking automation.In this paper,based on the YOLOv5 target detection algorithm,the ShuffleNet lightweight network structure is used to replace the feature ex- traction network of the original model,and an attention mechanism in the direction of the SE channel is added after the feature map extracted by the backbone network.Combining the EloU and Alpha-IoU loss functions,an a-EIoU loss function was designed,given a value of 3 for the parameter a,to unify the exponentiated IoU loss function,whereby a more accurate bounding box regression and object detection was obtained.The improved model in this paper achieved a mean detection accuracy of 97.6%on the average strawberry small object dataset,with 99.4%accuracy for ripe straw- berries,and improved mAP by 5.4%,2.9%and 1.1%compared to YOLOv3,YOLOv4 and YOLOv5 respectively. The model recognises images transmitted at 125 fps,an improvement of 38 fps over the original YOLOv5 model.The experimental model is more adaptable to mobile deployment and provides some theoretical basis for the automation of strawberry picking recognition.