Research on Detection Algorithm of Flooded Power Plant Based on Improved YOLOv5 Algorithm
In order to quickly and accurately detect and identify phenomena such as power plant flooding or equipment leakage,by using regional context information to supplement feature information,and multi-scale detection method to partially fuse shallow location infor-mation,an image detection algorithm for flooded power plants based on improved YOLOv5 is proposed.In addition,a dataset of water damage and leakage of power plant equipment for the phenomenon of flooded power plants is constructed data enhancement strategy is used.Experimental tests show that the detection effect of the algorithm is significantly improved,compared with the flooded power plant model based on the original YOLOv5 algorithm,the mAP value has increased by 5.24%,which meets the actual needs of the project and has high practicability.
flooded power planttarget detectiondeep learningYOLOv5 algorithm
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