A real-time object detection algorithm for mobile application on a mobile terminal
Aiming at the problems of large memory consumption and low precision of object detection algorithm deployed on a mobile terminal,a lightweight object detection network with improved attention mechanism is proposed based on NanoDet model.Firstly,the attention module is designed for double pools on the channel and double splits in space,so as to enhance the network's ability to focus on the region of interest without increasing the computing consumption as much as possible.Secondly,dilated convolution and Mish function are introduced to increase the receptive field and feature discrimination ability of the network,and reduce redundant down-sampling unit structures to speed up the real-time performance of the network.Finally,experimental verification on MS COCO2017 data set and Android devices shows that the proposed algorithm can improve the detection accuracy under a few model parameters,and ensure the detection speed of 30 frames per second on mobile terminals.The effect is better than that of lightweight networks such as YOLO series,and it is more suitable for real-time target detection scenarios on mobile terminals and embedded devices.
万方数据
维普
