Detection of floating debris in rivers is of great significance for ship autopilot and river cleaning, but the existing methods in targeting floating objects in the river with small target sizes and mutual occlusion, and less feature information lead to low detection accuracy. To address these problems, this paper proposes a small target object detection method called PAW-YOLOv7 based on YOLOv7. Firstly, in order to improve the feature expression ability of the small target network model, a small target object detection layer is constructed, and the self-attention and convolution hybrid module (ACmix) is integrated and applied to the newly constructed small target detection layer. Secondly, in order to reduce the interference of the complex background, the Omni-dimensional dynamic convolution (ODConv) is used instead of the convolution module in the neck, so as to give the network the ability to capture the global contextual information. Finally, the PConv (partial convolution) module is integrated into the backbone network to replace part of the standard convolution, while the WIoU (Wise-IoU) loss function is used to replace the CIoU. It achieves the reduction of network model computation, improves the network detection speed, and increases the focusing ability on the low-quality anchor frames, accelerating the convergence speed of the model. The experimental results show that the detection accuracy of the PAW-YOLOv7 algorithm on the FloW-Img dataset improved by the data extension technique in this paper reaches 89.7%, which is 9.8% higher than that of the original YOLOv7, the detection speed reaches 54 frames per second (FPS), and the detection accuracy on the self-built sparse floater dataset improves by 3.7% compared with that of YOLOv7. It is capable of detecting the tiny floating objects in the river channel quickly and accurately, and also has a better real-time detection performance.