A lightweight target detection algorithm based on Mobilenet Convolution
This addresses the cost and realizability issues for ground applications by balancing accuracy and computational efficiency, though it is incremental as it builds on existing MobileNet and CNN methods.
The paper tackled the problem of high computational cost in deep learning-based target detection by introducing a lightweight algorithm using MobileNet as backbone, achieving a processing speed of 30fps on an RTX2060 card for 320*320 resolution images.
Target detection algorithm based on deep learning needs high computer GPU configuration, even need to use high performance deep learning workstation, this not only makes the cost increase, also greatly limits the realizability of the ground, this paper introduces a kind of lightweight algorithm for target detection under the condition of the balance accuracy and computational efficiency, MobileNet as Backbone performs parameter The processing speed is 30fps on the RTX2060 card for images with the CNN separator layer. The processing speed is 30fps on the RTX2060 card for images with a resolution of 320*320.