# A YOLOW Algorithm of Water-Crossing Object Detection
## 2 Related Work
### Multiscale Object Detection  
Multiscale object detection poses a significant challenge, as it requires detecting objects of various sizes. Two common approaches for addressing this challenge are image pyramids [48] and FPNs (Feature Pyramid Networks) [49].  
The former adjusts the input image to multiple scales through image pyramids and train a separate detector for each scale. However, constructing and processing image pyramids requires additional computational resources, and the features of each scale are processed independently, which may lead to insufficient information dissemination.  
To address these issues, the SNIP [50] algorithm has been proposed. SNIP selectively performs backpropagation based on the object size in each detector, reducing training time and improving efficiency. This selective backpropagation method can reduce unnecessary calculations while improving the accuracy of the model. In addition, in order to further improve efficiency, the SNIPER [51] algorithm was proposed, which further improves the efficiency of SNIP by processing the contextual area around each object instance instead of every pixel in the image pyramid.  
On the other hand, FPN (Feature Pyramid Network) utilizes the inherent multiscale features in convolutional layers and combines them through horizontal connections in a top-down structure. This enables FPN to better preserve and utilize fine-grained details from lower-level features. The core idea of FPN is to combine lower-level detail features with higher-level semantic features through top-down paths and horizontal connections. This method can promote cross-scale feature propagation and integration of contextual information, thereby improving the accuracy of object detection. FPN combines horizontal connectivity and upsampling operations to fuse features of different scales together, generating feature maps with different scale information, thereby helping detection algorithms better adapt to objects of different scales.  
In order to enhance the information flow in FPNs, the PANet and BiFPN algorithms were introduced. They utilize shorter paths to improve the exchange of feature information, thereby enhancing the multiscale representation ability of features and the transmission ability of semantic information. In addition, the SAN (Scale Aggregation Network) algorithm maps multiscale features to the scale-invariant subspace, enhancing the robustness of the detector to scale changes, and thus mapping features of different scales to the same space, further improving the generalization ability and robustness of the model.  
In contrast, image pyramids require additional computational resources and independently process features at each scale, which may limit the dissemination and utilization of information. FPN retains fine-grained details of lower-level features, promotes cross-scale feature propagation, and integrates contextual information through horizontal connections.  
SPD [52] is a multiscale object detection method different from image pyramids and FPNs. It eliminates nonstep convolution and nonmaximum pooling operations, thereby preserving the original features. These methods have demonstrated significant improvements in multiscale object detection performance in various real-world applications.  
On this basis, this paper proposes an improved SPDCS module that introduces convolutional attention operation on the basis of SPD, while processing the original image to further preserve the original features. Compared with SPD, SPDCS can retain more channel count features, thereby improving the multiscale representation ability of features and the transmission ability of semantic information.