Optimization and Implementation of Tomato Detection Algorithm based on YOLOv7
Tomato fruit detection is a key issue that needs to be addressed in order to achieve mechanization and automation of tomato harvesting.In response to the complex background,dense fruit distribution,and leaf obstruction in the tomato growth environment,an optimized YOLOv7 mature tomato recognition model was proposed.Based on the YOLOv7 model,the main network's ELAN module was replaced with a P-ELAN module to reduce network parameters and computational load,while enhancing the network's feature extraction capability.Additionally,an LSK attention mechanism was added in front of the detection head to dynamically adjust the receptive field using the feature extraction module,more effectively handling the differences in background information required for different targets.Finally,the EIoU loss function was introduced to more effectively guide the model in learning more accurate bounding box predictions,thereby accelerating the convergence of prediction boxes and improving the regression accuracy of prediction boxes.The improved algorithm not only has high recognition accuracy but is also more lightweight,making it well-suited for application in mature tomato harvesting scenarios.
tomato recognitionYOLOv7attention mechanismP-ELANloss function
万方数据
维普
