Abstract: Pedestrian detection in intelligent community scenarios needs to accurately recognize pedestrians to address various situations. However, in the face of the scenarios of occluded and long-distance person, the existing detectors have some problems such as missed detection, error detection and large model. To address the above problems, this paper proposes a pedestrian detection algorithm, ME-YOLO(Multiscale Efficient-YOLO), based on YOLOv8. An efficient feature extraction module is designed to make the network learn and capture pedestrian features better, which reduces the number of network parameters and improves the detection accuracy. A reconstructed detection head module reintegrates detection layer to enhance the network's recognition ability of small targets and effectively detect small target pedestrians. The bidirectional feature pyramid network is introduced to design a new neck network, and the expanded residual module and weighted attention mechanism expand the receptive field and learn pedestrian features with emphasis, alleviating the problem of network insensitivity to occlusion of pedestrians. Compared with the original YOLOv8 algorithm, ME-YOLO increases the AP50 by 5.6 percentage points and reduces the number of model parameters by 36% and compresses the model size by 40% after training and verification based on the CityPersons dataset, which also increases the AP50 by 4.1 percentage points and the AP50:95 by 1.7 percentage points on the TinyPerson dataset. The algorithm not only significantly reduces model parameters and size but also effectively improves detection accuracy. It holds considerable application value in intelligent community scenarios.

摘要： 智慧社区场景下的行人检测需要精准识别行人以应对各类情况的发生，然而面对遮挡和远距离行人的情景，现有检测器会出现漏检、误检，以及模型过大不易部署的问题。针对以上问题，提出了基于YOLOv8的行人检测算法ME-YOLO(Multiscale Efficient-YOLO)。设计一种高效特征提取模块EM，使得网络更好学习行人特征和捕捉行人特点，减少了网络参数量的同时提高了检测精度。提出一个重构的检测头模块，重新整合后的检测层增强了网络对小目标的识别能力，有效检测小目标行人。引入双向特征金字塔网络来设计新的颈部网络BDR-FPN，利用扩张残差模块和附权注意力机制来扩展感受野和有所侧重地学习行人特征，缓解网络对遮挡行人不敏感问题。所提算法在CityPersons数据集上进行训练和验证，相比较原算法YOLOv8，ME- YOLO算法的AP50提高了 5.6个百分点，模型参数量减少了41%，模型大小压缩了40%。在数据集TinyPerson上验证算法的有效性和泛化性，AP50提高了4.1个百分点，AP50:95提高了1.7个百分点。该算法在大幅度减少模型参数和大小的同时有效提高了检测精度，在智慧社区场景中有较好的应用价值。