Lightweight Passion Fruit Detection Method Based on Improved YOLOv7-Tiny
Accurate and fast detection of fruits in orchards is one of the key tasks for intelligent agricultural approaches,such as fruit yield prediction and automated harvesting.A lightweight detection method based on an improved YOLOv7-Tiny is proposed in this paper to address the current issue of large parameters and FLOPs in object detection models.The method is specifically designed for detecting passion fruit in complex orchard environments,aiming to enhance real-time applicability on embedded devices.Firstly,the Omni-dimensional Dynamic Convolution(ODConv)is employed in the backbone network to enhance its feature extraction capability,thereby increasing the mean Average Precision(mAP)by 2 percentage points.Furthermore,to reduce the parameters and FLOPs of the neck network,the GMConv lightweight module is proposed by integrating the GhostNet network and the MobileOne network.The parameters and FLOPs have decreased by approximately 30％and 20％,respectively,and the model's FPS has increased by around 50 frame/s.Experimental results on the passion fruit dataset reveal that,compared with YOLOv7-Tiny,the parameters and FLOPs of the improved algorithm have decreased by 32.1％and 25.4％respectively,while the mAP has increased by 2.6 percentage points.With the reduction of FLOPs and parameters,the improved algorithm further enhances detection accuracy,offering theoretical research and technical support for deployment on embedded devices.
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