Fruit Target Detection Method based on Improved YOLOv7
At present,fruits are widely planted,but most of the fruit classification is done manually,which consumes a lot of labor costs,and a few machine recognition also has problems such as slow speed and low accuracy.To solve the problem of low efficiency of target detection and recognition,a fruit target detection algorithm based on improved YOLOv7 algorithm was proposed.By introducing ECA attention mechanism,the relevance of channel dimensions was enhanced,and the expression ability and learning effect of the model were improved;PConv was used instead of partial convolution structure,and redundant computing and memory access were reduced to extract spatial features more effectively;The loss function uses MPDIoU,which enhances the gradient differentiability for traditional IoU,facilitating the training and optimization of image segmentation tasks.The improved YOLOv7 algorithm can accurately recognize fruits by improving the precision and average accuracy by 4％and 3％respectively.
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