Aiming at the problems of large amount of computation and complex model in target detection algorithm,which make it difficult to deploy in application scenarios with limited computing resources on embedded platform.The lightweight target detection method based on optimized RetinaNet is proposed with adaptive feature fusion.Firstly,the proposed algorithm refers to the Ghost Module in GhostNet to reduce the number of model parameters.By means of a spatial feature fusion mechanism,the scale invariance of features is improved.Secondly,the idea of structural reparameterization is integrated to increase the depth of training,realize multi-branch training,single-branch training,and better improve the detecting performance of the model.The method is evaluated on two common target detection datasets,PASCAL VOC2007 and COCO.With an average accuracy of 54.1%,better than that of RetinaNet.The experimental results show that the memory taken by the proposed method is 170.71MByte,which is 44.27%of the memory taken by the RetinaNet,indicating that the proposed algorithm can greatly improve the network inference speed without ensuring the accuracy.
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