# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 1 Introduction  
With the acceleration of urbanization, it becomes imperative to count and locate individuals in public spaces when an emergency occurs to ensure efficient and timely evacuation [1; 2; 3]. Although crowd person detection technologies are currently widely employed in various domains such as pedestrian flow monitoring, intelligent transportation systems, and security surveillance, scenarios featuring high crowd density, such as railway stations, shopping malls, and traffic intersections, are facing significant challenges. That is, on one hand, the diverse postures and prevalent occlusion and overlapping among persons lead to scarcity and ambiguity of visible features, thereby complicating the feature extraction process for the network [4; 5; 6]. Consequently, feature loss emerges as a pivotal issue in crowd detection. On the other hand, the existing detection models consume substantial computational resources. Therefore, the optimization of crowd person detection models, aiming at enhancing precision and real-time performance in complex scenarios, is important for public safety and enhancing urban management efficiency. As deep learning algorithms develop, they have been leveraged to automatically learn deeper and more intricate features from images, demonstrating robust generalization capabilities. Consequently, they are widely applied across diverse tasks such as image classification, target tracking, and object detection. In the realm of object detection algorithms, there are typically two major kinds of classification. Firstly, based on the presence of anchor boxes, object detection algorithms can be categorized into anchor-based andanchor-free. Secondly, they can also be divided into one-stage and two-stage detection algorithms, distinguished by their distinct methodologies and workflows in accomplishing the detection task. Among these, anchor-based algorithms, originating from the Faster R-CNN [7] algorithm, generate candidate object regions providing validation information for object detection, and serve as a benchmark for the network to learn and predict object locations and sizes. Notably, the mainstream ATSS (Adaptive Training Sample Selection) [8] significantly enhanced the performance of both anchor-based and anchor-free detection algorithms, bridging the gap between them. Meanwhile, anchor-free algorithms predict the center point, four corners of the bounding box, keypoints, or boundaries of objects directly, without predefined anchor boxes. Its exemplary algorithms include FCOS [9] and CornerNet [10]. The former one directly predicts the object class, bounding box regression, and center point confidence for each location, while the other determines bounding boxes by predicting the top-left and bottom-right keypoints of objects. Moreover, regarding two-stage detection algorithms, such as R-CNN [11], Faster R-CNN [7], and Dynamic_RCNN [12], they first generate numerous candidate regions that may contain objects and then classify these regions. However, their complex training process and relatively long detection time render them unsuitable for real-time applications for emergency management. In contrast, one-stage detection algorithms like RetinaNet [13] and the YOLO series are better in time-consuming performance. RetinaNet employs focal loss as its loss function, reducing the weight of easy samples during training, enabling the model to focus more on difficult-to-classify samples. YOLO directly predicts object locations and classes, requiring minimal computation and significantly boosting detection speed. Therefore, YOLOv5, as a typical single-stage, anchor-based detection algorithm, inherently satisfies both real-time and precision requirements, making it an ideal choice for wide application in real-time object detection tasks. However, scenarios involving dense object detection are prone to interference from various factors like background noise and occlusion, making it challenging for detection algorithms to efficiently capture effective feature information from the images.