A Multi-target Detection Method Based on Dilated Convolutional UAV Images Fused with Location Attention
Aiming at the problem of multi-category target detection based on UAV images,this paper proposed a convolutional neural network detection model fused with location attention.At the feature extraction end,the cascaded dilated convolutional kernel group was used to realize feature extraction,and the extracted features are further screened by the position convolution module;in order to enhance the complexity of the information in the feature map participating in the detection,the multi-scale feature fusion end is used to output features of the upper layer.The graphs are fused and connected by unsampling,and it finally outputs feature maps of four scales to participate in the final detection.The test results show that the model was superior to the other comparison models in terms of detection accuracy,at the same time shows good generalization ability in multiple scenarios,and can achieve real-time detection of targets in the test environment.
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