A Robust Detection Method for Illegal Buildings in Urban-Rural Fringe Areas Based on Improved RetinaNet Deep Learning Network
Aiming at the problems such as diverse types of illegal buildings,a large number of small objects to be detected and low detection accuracy in urban-rural fringe areas,etc.,a detection method for illegal buildings based on improved RetinaNet deep learning network is proposed.Firstly,the CA attention mechanism is embedded in the backbone feature extraction network ResNet50 to enhance the network's perception of small objects.Secondly,the parallel dilated convolution(DCB)module is introduced into ResNet50 to achieve multi-scale feature fusion and reduce the algorithm's missed detection rate.Finally,the activation function is replaced by GeLU to accelerate model convergence and improve model stability.Experimental results show that the average accuracy of the im-proved model reaches 93.28%,and the number of parameters is 3.920×107,which can provide a theoretical basis for the real-time monitoring and demolition of illegal buildings in urban-rural fringe areas.
deep learningRetinaNetattention mechanismillegal building
国家科技期刊平台
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