Lightweight safety helmet detection method and system for mobile terminal

Abstract

The invention provides a lightweight safety helmet detection method and system for a mobile terminal. The method comprises steps of obtaining related image information; obtaining a detection result of the safety helmet according to the obtained related image information and a preset safety helmet detection model, wherein the safety helmet detection model is obtained by improving a Darknet53 residual network in a YOLOv3 network, specifically, the number of channels is divided into a first part and a second part according to a CspNet network architecture, and the first part is not subjected to any convolution operation; the second part is subjected to convolution and Concat operation twice, the feature map after convolution operation and the second part are superposed, and the superposed feature map and the first part are subjected to concat connection; according to the lightweight target detection algorithm provided by the invention, the network model structure is improved, the purposes of simple network structure and small parameter quantity are realized, the influence on the time delay problem is relatively small, and the lightweight target detection algorithm is suitable for mobile equipment or scenes which cannot be applied by the original algorithm.

Description

technical field [0001] The disclosure belongs to the technical field of helmet detection, and in particular relates to a lightweight helmet detection method and system for a mobile terminal. Background technique [0002] With the advancement of science and technology, the development of deep learning has made it possible to implement many computer vision tasks. These tasks have penetrated into all walks of life, such as industrial safety, including tasks such as helmet wearing detection, falling object detection and abnormal accident detection (pedestrians). Can't afford to fall, etc.) and so on. [0003] The inventors of the present disclosure found that the network model also has the following problems in the application of helmet detection: [0004] 1. Due to the complex network structure and large amount of parameters, the weight file of the network model is large and occupies a large memory space. It is difficult to deploy applications on some mobile devices, such as s...

AI Technical Summary

Problems solved by technology

[0004] 1. Due to the complex network structure and large amount of parameters, the weight file of the network model is large and occupies a large memory space. It is difficult to deploy applications on some mobile devices, such as smart phones, tablet computers and smart cameras;

[0005] 2. The existing network model is deployed to the mobile terminal to realize the helmet detection. Because the network model has the problem of model loss calculation and convergence difficulty, the loss of model calculation accuracy and speed is high

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