## 1 Introduction  
Pedestrian detection is a prominent research topic in computer vision, focusing on accurately identifying pedestrians and determining their locations. As a fundamental technology, pedestrian detection algorithms have been extensively adopted in both academic research and industrial applications. Current technical challenges primarily lie in occlusion and scale variation issues in dense scenarios. In crowded environments, pedestrian targets often exhibit small scale and heavy mutual occlusion, posing substantial detection challenges. Key difficulties include:  
* Occluded pedestrians exhibit sparse or blurred features, hindering effective feature extraction by neural networks.
* Overcrowding leads to reduced individual target resolution, increasing missed-detection rates.
* Complex background features in dense scenes impair accurate classification and localization, causing false positives.  
To address occlusion challenges, [PERSON] et al. [1] proposed partitioning pedestrian proposals using prior ratios and extracting region-specific features to recover occludedlocal features through body structure information. [PERSON] [2] developed FC-Net, a feature learning method that emphasizes visible pedestrian features while suppressing occluded regions through a novel self-activation mechanism. [PERSON] et al. [3] enhanced Faster R-CNN [4] with UAST-RCNN, improving global receptive fields, feature sample quality, and multi-scale training to boost occlusion handling.  
[PERSON] et al. [5] introduced a variational Bayesian autoencoder-based algorithm that models dense proposals as latent variables, enhancing a conventional detector into a variational pedestrian detection framework. [PERSON] et al. [6] observed persistent missed detection of small-scale pedestrians despite dense proposal predictions. For scale variation issues, [PERSON] et al. [7] enhanced multi-scale feature optimization by highlighting scale-specific features and enabling cross-layer feature sharing between deep and shallow networks. [PERSON] et al. [8] proposed a feature alignment pyramid network for dense image prediction, aligning upsampled features with local features via learned pixel offsets. While YOLOv8 [9,10] (2023) achieves real-time performance with high accuracy, current deep learning methods still struggle with missed detection and false alarms in dense pedestrian scenarios. Existing feature fusion and reconstruction approaches show limited performance gains in noisy, crowded environments.  
To overcome these limitations, this paper proposes an enhanced YOLOv8-based algorithm focusing on multi-scale occluded feature enhancement and localization capability improvement. Key contributions include the following.  
1. We designed a C2f_D2 CN module to address the challenge of extracting small-scale pedestrian features in dense scenarios. This module enhances feature extraction capability by incorporating a backbone network improved with deformable convolution and dynamic convolution. Additionally, a multi-scale linear spatial attention module was designed to enhance the visible features of occluded pedestrians while suppressing interference from complex backgrounds.
2. We integrated a small-scale pedestrian detection head into the neck of the YOLOv8 network, effectively improving the detection accuracy of small-scale pedestrians in dense scenarios. Additionally, to improve training efficiency, we designed a novel loss function named DFL-SIoU, which enhances model convergence and achieves better bounding box regression performance.
3. We conducted experiments on two challenging dense pedestrian datasets, CrowdHuman and WiderPerson, and the detection accuracy showed significant improvement compared to most existing methods.  
This work advances dense pedestrian detection by systematically addressing feature extraction, scale sensitivity, and localization precision in complex real-world scenarios.