Application of Improved YOLOv7 Network in Recognition of Construction Waste in Remote Sensing Imagery
An improved YOLOv7-based object detection model to enhance the efficiency of identifying construction waste in high-resolution remote sensing images is proposed in this paper.The method utilizes data from the Gaofen-2 satellite imagery.Firstly,the model's bounding box regression is optimized using SIoU to expedite the convergence speed.Secondly,the funnel activation function FReLU is employed to expand the receptive field of the convolutional layers,thereby improving the model's feature extraction capabilities.Finally,depth-wise separable convolutional kernels are utilized to enhance detection accuracy while reducing model parameters.Experimental results demonstrate that the improved YOLOv7 model achieves a 5.8％increase in average precision,6.4％improvement in accuracy,and 8％enhancement in recall compared with other models.It exhibits excellent recognition performance,offering a reliable approach for construction waste identification in remote sensing images.
loss functiondepth-wise separable convolutionactivation functionconstruction waste recognitiontarget detection
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