In the paper,a mask wearing detection system based on YOLOx is proposed.Firstly,the model improves the input module by introducing bidirectional low-light adaptive network and low-light object detection techniques,thus enhancing its robustness in complex environments.Secondly,mask wearing detection under various shelters is added to improve the target identification accuracy in complex environments.Finally,to test the effect of the model,comparative experiments are conducted based on the data set of mask-wearing.The experiment results show that the improved algorithm increases the mAP of mask-wearing recognition by about 1.35%,reaching 94.75%, according to the data set.Moreover,the detection effect in complex,blurred,and low-light environments gets much better,featuring strong generalization ability.
Key words
YOLOx /
target detection /
mask wearing detection /
weak light adaptation
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