Water Level Line Detection Algorithm Based on Improved PIDNet
PIDNet is a semantic segmentation model composed of three branch networks,which maintains excellent segmentation accuracy in many competitive datasets.However,the shortcomings of multiple downsampling in the integral branch and the redundancy of multi-branch feature fusion in the pyramid pooling module limit the improvement in the accuracy of the algorithm.Existing algorithms for water level line detection suffer from shortcomings that result in the loss of local detailed information,thereby reducing their ability to detect water edges.To alleviate this problem,a water level line detection algorithm based on improved PIDNet is proposed.First,a Lightweight Pixel Enhancement Module(LPEM)combined with channel attention is designed to perform pixel enhancement to reduce local information loss during integral branch downsampling.The pyramid pooling module is then reconfigured to reduce the number of parallel branches by reducing the pooling output feature size.Combining channel attention during feature fusion further enhances the ability to focus feature attention and improves the water level line edge segmentation accuracy.In addition,this study combines a multi-scene river dataset to overcome situations in which the detected water level line position will shift or even break when the scene is complicated.The experimental results show that the method(S and M)in this study improves three performance metrics relative to the original algorithm(S and M)in the water level line detection task.Considering method(M)in this study as an example,the Pixel Accuracy(PA)is improved by 1.47 percentage points,the Mean Intersection over Union(mIoU)is improved by 1.04 percentage points,and the detection delay is reduced by 0.9 ms.
semantic segmentationwater level line detectionpyramid pooling moduleattentionmulti-scene
万方数据
维普
