基于YOLOX的輕量化目標檢測算法及其應(yīng)用

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中圖分類號：TP391 文獻標志碼：A

Lightweight object detection algorithm based on YOLOX and its application

CHAI Weizhen， WANG Chaoli， SUN Zhanquan （School of Optical-Electrical and Computer Engineering， University of Shanghai for Science and Technology， Shanghai 200093， China）

Abstract： Object detection algorithms are widely used in the field of production safety. To address the problems of slow detection speed and low detection accuracy in complex construction environments， an improved YOLOX detection algorithm was proposed. First， based on the lightweight convolution module Ghost moudle， the backbone network was reconstructed to compress the model parameters and computational complexity， thereby improving detection speed. Second， embedding the coordinate attention mechanism at the output of the backbone network to enhance the model's ability to learn key position information. Finally， recursive gated convolution was introduced into the neck network to enhance the model's spatial position perception ability and capture long-range dependencies in the image. The improved model are experimentally validated on the Pascal VOC and SHWD datasets， comparing with the baseline model， mean average precision increase by 1.69% and 1.1% respectively， model parameter count decrease by 18.8% ， computational load decrease by 23.3% ， and frame rate increase by 7.6% . Deploying the purposed model on terminal devices can be applied to real-time monitoring and detection in construction environments.

Keywords： object detection; lightweight; coordinate attention; recursive gated convolution

目標檢測算法在生產(chǎn)安全領(lǐng)域應(yīng)用廣泛，它可以代替?zhèn)鹘y(tǒng)的人工監(jiān)督方法，節(jié)約人力物力。（剩余14537字）