Lightweight apple real time detection algorithm based on YOLOv7-tiny
In response to the problems such as high complexity of the natural environment in which apples grow and too large network model which is difficult to deploy on mobile devices,a lightweight real-time apple detection method based on YOLOv7-tiny is proposed.This algorithm introduces the CG-Block module to replace the partial convolution of the original YOLOv7-tiny network,modifying the ELAN-tiny structure of the original network,greatly reducing the network size and improving detection accuracy.Using Mish activation function instead of the original activation function improves the features extraction ability of network.The use of CARAFE lightweight upsampling operator further enhances the feature fusion ability of the network.The experimental results show that compared with the original algorithm,the improved algorithm improves mAP@0.5 by 1.9%,accuracy by 4.1%,parameter count by 45.4%,computational complexity by 46.2%,model size by 43.9%,and FPS by 196.1 f/s.The improved algorithm not only maintains good real-time performance,but also improves detection accuracy,greatly reduces network scale,and adds feasibility to the deployment of network models on mobile devices.
applelightweightreal time detectionactivation functionupsampling operatormobile deployment
万方数据
维普
