Optimization of Actinidia chinensis Fruit Recognition based on Improved YOLOv7
In order to solve the problems of severe occlusion caused by overlapping fruits and susceptibility to leaf influence in the recognition process of Actinidia chinensis fruit,A.chinensis fruit image dataset was estab-lished under different sunlight conditions.Three improvements were made to the YOLOv7 model:replacing the convolutional module of the Backbone part with the GhostConv module,reducing the number of model parame-ters while maintaining the original accuracy;to address the significant overlap between A.chinensis fruits,a Non Maximum Suppression NMS(Soft NMS)strategy is introduced to improve the accuracy of detection box regres-sion;integrating SimAM attention mechanism to enhance the model's ability to extract high-density A.chinensis fruit features.Through comparative experiments,it was shown that the optimized model increased mAP value by 12.7%and detection speed by 106.8 frames/s compared to Faster RCNN.The overall performance is good and meets the real-time recognition needs of machines for A.chinensis fruit.