Directional Target Detection Algorithm Based on YOLOv3
In order to solve the problem that the YOLOv3 target detection algorithm cannot detect the directional target of rotating objects,this paper proposes a directional target detection algorithm based on YOLOv3.Firstly,this method used the multi-dimensional coordinate to align the training set to suit the training of the network.Scoendly,the rectangular box output from the network was optimized using the minimum outer rectangle to obtain a more accu-rate fit of the detection box.Next,the loss function of the network was improved to adapt it to the regression of multi-dimensional coordinates.Finally,the improved network wais trained.The experimental results on the UCAS-AOD dataset show that the ability of target detection is significantly improved after the improvement,with a 6.1%increase in accuracy and a 3.2%increase in recall over the original YOLOv3 algorithm.
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