Research And Implementation Of Lightweight Object Detection Network And System

As society becomes more intelligent,people want to experience the convenience of artificial intelligence.Object detection is one of the research hotspots in the field of computer vision.This paper studies and implements a lightweight object detection network based on deep learning,and builds a C/S structure cloud detection system,which involves the practice and application of deep learning,cloud server,and mini-program technology.The main work and results are as follows:Two lightweight backbone networks and a lightweight object detection network based on attention mechanism were designed and built.In addition,with the help of secondary compression and parameter quantization,faster object detection is achieved on the CPU platform.The model achieved 71%detection accuracy(mAP)and 3.7 images/second inference speed on the VOC2007 test set.Taking the above model as the core module,a cloud detection system of C/S architecture based on the cloud server and WeChat mini-program was developed.After system requirements analysis and design,deployment of cloud server Web Server and algorithm sub-modules,development of WeChat mini-program logic layer and rendering layer,module open-loop test and system closed-loop test,the system is realized with expected function and basic commercial performance which finish the detection within 2 seconds.The system also supports multi-algorithm,cross-platform,computing resource expansion,easy to maintenance and promotion,which has certain application prospects.