# YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
## 1 Introduction  
With the rapid advancement of computer vision technology, object detection has emerged as a pivotal task within the field, drawing extensive research interest. This task aims to autonomously detect and accurately localize target objects in images or videos, laying the groundwork for higher-level visual tasks such as scene understanding and object tracking. In recent years, the rise of deep learning, particularly with the introduction of convolutional neural networks (CNNs) [1], has catalyzed significant progress in this area, leading to the development of various efficient and high-precision algorithms. Traditional object detection methods, such as Viola-Jones [2], Haar-like [3] features, and deformable part models (DPM) [4], achieved notable success in earlier stages but faced limitations in complex environments, resulting in deficiencies in efficiency and accuracy. The advent of deep learning has positioned CNN-based algorithms as the mainstream approach. In two-stage methods, candidate regions are generated first, identifying potential object-containing areas, followed by classification using CNNs. Notable two-stage detectors include Faster R-CNN [5] and SPP-Net [6]. Algorithms like R-CNN [7] and Fast R-CNN [8] also play critical roles in this category. Although these methods boast high accuracy, they often suffer from substantial computational complexity and slow processing speeds, making them unsuitable for real-time detection requirements. In contrast, single-stage algorithms eliminate the need for region proposals, directly extracting features to predict object classes and locations, thereby significantly enhancing processing speed. Prominent examples of single-stage methods include SSD [9], RetinaNet [10], and the YOLO series [11]. The YOLO (You Only Look Once) family of algorithms holds a crucial position in object detection. Since the initial introduction of YOLO, multipleversions have been released, each with its advantages: YOLO [12] enabled real-time detection but had limited performance in small object detection; YOLO9000 [13] introduced a joint training mechanism to enhance applicability; YOLOv3 [14] improved accuracy through multi-scale feature maps; YOLOv4 [15] achieved a favorable balance between performance and speed; YOLOv5 [16] gained popularity for its lightweight design and user-friendliness; and YOLOv7 [17] significantly enhanced small object detection capabilities through improved feature extraction and fusion methods. However, despite these algorithms' notable advancements in speed and accuracy, they still face various challenges in practical applications. In pedestrian detection tasks, the limitations of the YOLO algorithms become particularly pronounced. First, the YOLO series exhibits deficiencies in detection accuracy for small and occluded targets, especially in complex scenes where contextual information often remains inadequately captured [18]. Traditional YOLO models are prone to missing small objects and experience significant recognition failures due to occlusions among pedestrians in crowded environments. Moreover, the YOLO algorithms struggle with highly overlapping targets, as conventional non-maximum suppression (NMS) methods frequently fail to distinguish closely situated bounding boxes, resulting in target loss and limiting their performance in practical scenarios. To address these challenges, this paper proposes YOLO-ESL, an improved pedestrian detection algorithm built upon the existing YOLO framework, aimed at enhancing detection accuracy and robustness. The primary contributions are as follows:  
* To enhance small object feature extraction in YOLOv7, we developed a lightweight ELAN-SA module, enabling richer semantic capture and improved performance. * To address information redundancy and scale inconsistencies in feature fusion, we introduced the LGA module, which optimizes multi-scale combinations using local and global attention mechanisms.