Aiming at the problems of variable morphology， low resolution and limited information of small-target water-floating garbage， which lead to unsatisfactory detection results， an improved Faster-RCNN （Faster Regions with Convolutional Neural Network） water-floating garbage detection algorithm was proposed， namely MP-Faster-RCNN （Faster-RCNN with Multi-scale feature and Polarized self-attention）. Firstly， a small-target water-floating garbage dataset in Lanzhou part of the Yellow River was established， the combination of atrous convolution and ResNet-50 was used as the backbone feature extraction network instead of the original VGG-16 （Visual Geometry Group 16） to expand the perception field for extracting more small-target features. Secondly， two layers of convolutions of 3×3 and 1×1 were set in the Region Proposal Network （RPN） by using multi-scale features to compensate for the feature loss caused by a single sliding window. Finally， polarized self-attention was added before RPN to further utilize multi-scale and channel features to extract finer-grained multi-scale spatial information and inter-channel dependencies to generate a feature map with global features， achieving more accurate target box localization. Experimental results show that compared with the original Faster-RCNN， MP-Faster-RCNN can effectively improve the detection accuracy of water-floating garbage with a mean Average Precision （mAP） improvement of 6.37 percentage points， the model size is reduced from 521 MB to 108 MB， and the convergence speed is faster under the same training epoch.