# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 1 Introduction  
Object counting is a significant research direction in computer vision, focusing on estimating the number of target objects in images or videos. As a fundamental and critical task, it finds extensive applications in various domains, such as intelligent agriculture [1], drone applications [2], medical research [3], and traffic monitoring [4]. The importance of object counting is reflected in several aspects. On the one hand, it provides direct data support for resource allocation and optimization in real-world scenarios, such as counting the number of customers in a store to enhance service quality. On the other hand, compared to tasks such as object detection or segmentation, object counting offers lower annotation costs and greater adaptability to diverse scenarios. This paper primarily investigates the counting of target objects in images. However, despite the wide applications and great significance of object counting, current object-counting methods still face severe challenges in practical applications, especially when objects exhibit various different orientations or when annotated samples are scarce. These challenges are closely related to the limitations of existing methods and the specific requirements of real-world applications. This paperprimarily aims to address these challenges by proposing an effective solution for the counting of target objects in images. In recent years, significant progress has been made in object-counting methods [5; 6; 7] for images. Based on their underlying principles and application characteristics, these methods can be categorized into three main approaches: classical image analysis [8], traditional feature-based methods [9; 10], and convolutional neural network (CNN)-based methods [11; 12; 13]. Classical image analysis relies on image preprocessing, feature extraction, and traditional image processing algorithms for object counting. It is most effective in scenarios where objects have regular shapes, simple backgrounds, and low object counts. Traditional feature-based methods involve manually extracting features from images and using machine learning algorithms for classification or regression to achieve object counting. These methods heavily depend on domain expertise for feature extraction, and their performance is often influenced by the scale and quality of the dataset. In contrast, CNN-based methods learn image features automatically through neural networks, eliminating the need for manual feature design. This gives CNNs greater adaptability and flexibility for object-counting tasks. CNNs have been widely adopted for object-counting tasks due to their ability to automatically extract object features from images and generate density maps for counting [14; 15]. However, most existing object-counting algorithms are trained and recognized using fixed feature dimensions, often neglecting the diversity of object angle variations. In practical applications, the fixed positions and angles of cameras result in objects within images exhibiting a wide range of orientations. For example, vehicles entering the frame from different angles exhibit significant variations in shape, size, and visible features in vehicle counting at intersections. Vehicles approaching head-on prominently display their front grilles and headlights, while those moving sideways or diagonally emphasize the vehicle's body length. Vehicles moving away reveal their rear, including the taillights and rear bumpers. This limitation of fixed feature dimensions restricts the performance of existing methods in dealing with objects with diverse angles, leading to inaccurate counting results. Additionally, object counting faces the challenge of insufficient training samples. Traditional methods require large annotated datasets, which are expensive and time-consuming, often involving annotating thousands of objects across numerous images.