Fire image recognition method based on improved YOLOv5s network model
A fire image recognition method based on improved YOLOv5s network model is proposed.Feature extraction network is improved by introducing attention mechanism to improve the learning ability of the model to learn about feature.Multi-scale detection mechanism is improved by adding large-scale detection layer,and K-Means clustering algorithm is implemented to improve the anchor box,which enhances recognition ability of the model for small targets.Test results on the experimental dataset show that the improved YOLOv5s network model has improved in terms of precision,recall and mean average precision(mAP)comparing with the original model.The mAP of improved model is 85.72％,and the frame rate reaches 54.66 fps.The improved model has a significant improvement in the level of confidence,the recognition effect on multiple targets and small targets is better,and the missed and false detections are effectively reduced.The proposed fire image recognition method is applicable for security monitoring systems and intelligent robots.
fire recognitionattention mechanismmulti-scale detectionYOLOv5s network model
NETL
NSTL
万方数据
维普
