Underwater vehicle target detection and experiment based on improved RetinaNet network
Aiming at the problems of serious image degradation and low target recognition rate in current underwater vehicle target detection methods,an underwater target detection method combining improved RetinaNet and attention mechanism is proposed.Firstly,RetinaNet backbone network is re-placed with DenseNet network,which retains more target features and reduces the number of parame-ters.Secondly,in order to increase the operation speed of the network model,the initial convolution is replaced by the depth separated deformable convolution,thus greatly reducing the parameters of the model.Finally,CBAM attention module is introduced to enhance features in space and channel dimen-sions,reducing the interference of underwater complex environment to target detection.The experimen-tal results of underwater robot grasping show that compared with the initial RetinaNet methods,The mAP value of the improved method can reach 81.9%,the parameters are 56.8 MB,and the detection speed is 16.8 frames.The improved method has excellent performance in underwater target detection.
万方数据
维普
