Target detection method in laser remote sensing images based on residual dense blocks
In order to improve the effectiveness of object detection,a method for object detection in laser remote sensing images based on residual dense blocks is proposed.Firstly,design a convolutional neural network based on re-sidual dense blocks.After designing the ReLU activation function and completing network training,based on the pre-liminary feature extraction results of noisy laser remote sensing images,use a single convolution to unfold the convolu-tional mapping process and extract potentially clean images.Then,through clustering processing,the saliency map of vehicle targets in the laser remote sensing image is obtained,and then the target information is detected using the es-tablished feature proportion relationship using the general law.The experimental results show that the application of this method effectively filters out noise in laser remote sensing images and accurately detects vehicle targets in laser re-mote sensing images.Compared to the three traditional methods,the minimum value of the mean error of the detection results of this method is only 0.015 6,indicating that this method effectively achieves the design expectations.
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