Application of Deep Learning Method in Safflower Picking Robot
In order to realize rapid and accurate recognition of flesh safflower in complex agricultural environment,a new method based on improved YOLOv5s was proposed.Based on YOLOv5s,a GPU-adapted lightweight Ghost module is in-tegrated to obtain a baseline model with lower complexity and faster network reasoning speed.CBAM attention mechanism is embedded into the baseline model to improve the performance of small objects in high frequency features.A Focal-EIoU loss function based on border width and height difference was established to improve the recognition rate of safflower under different occlusion conditions.Finally,experiments on a parallel safflower picking robot are carried out to verify the feasibility and reliability of the improved algorithm.The experimental results show that the mAP value of the improved Yolov5s model is improved by 1.94 percentage points compared with the original model.The parameters of the model and the detection speed of a single image are 3.52 MB and 0.06 s/amplitude respectively,the recognition success rate of robot vision system for picking safflower can reach 89.92%.
safflowerpicking robotdeep learningYOLOv5srecognition success of rate
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