# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 2 Related Works
### CNNed Object Counting  
Convolutional neural networks (CNNs), as a crucial branch of deep learning, have demonstrated remarkable potential in the field of object counting [15, 16, 17]. By leveraging their distinctive structures, including convolutional, pooling, and fully connected layers, CNNs can automatically learn feature representations from images, enabling precise object counting. The application of CNNs to object counting has evolved from initial explorations to widespread adoption.  
In the early stages, researchers primarily utilized CNNs for image classification and object detection [18, 19]. With continuous advancements in technology, CNNs were gradually applied to object counting. In recent years, the constant development of deep learning has led to notable advancements in CNN-based object-counting methods. Researchers have proposed various algorithms and network architectures. For instance, [PERSON] et al. [16] introduced a two-stage framework for estimating building counts to assess population density in a given area. This framework utilized CNN-based deep learning models to analyze low-resolution satellite images. The two stages of the framework employed different CNN variants for partitioning and counting, achieving encouraging accuracy in processing low-resolution satellite imagery. Similarly, [PERSON] et al. [17] proposed a vehicle-counting network based on a feature pyramid segmentation attention mechanism for accurate vehicle counting in highly congested scenes and the generation of high-quality vehicle density maps. The core innovation lies in employing convolution operations with kernels of varying sizes to obtain multi-scale feature mappings along the channel dimension. This approach enabled the model to focus on features at different scales, reducing errors caused by background information misestimation.  
However, most CNN-based object counting methods still face challenges when handling objects with diverse orientations. This is because their feature extraction and classification mechanisms typically rely on fixed templates or feature representations, limiting their adaptability to rotated objects. Such limitations directly impact the accuracy and robustness of object counting in real-world scenarios where objects may appear in various orientations. Traditional counting methods are often hindered by this issue, as they struggle to accurately extract features from objects with different orientations. This often hinders traditional counting methods, as their feature extraction and classification mechanisms typically rely on fixed templates or feature representations, limiting their ability to adapt to rotated objects. To address this issue, this study presents the SSA module, which performs slicing convolutions in multiple directions along the spatial dimension. This enables the model to learn the correlations and consistencies among objects from various perspectives. Additionally, the CAE module is introduced to mitigate the discrepancies in channel feature information caused by orientation variations, thereby improving the model's ability to capture object features effectively. By introducing these modules, our approach directly responds to the limitations of existing CNN-based object-counting methods, enhancing the robustness and accuracy of CNN-based object counting in scenarios with diverse object orientations. In conclusion, these innovations enhance the robustness and accuracy of CNN-based object counting in scenarios with diverse object orientations.