关键词: 公共安全, 无人机, 人脸检测, 密集人群, 轻量化网络

Abstract: High-precision face detection algorithms and lightweight counterparts suffer from opposing limitations in UAV deployment: the former are computationally prohibitive, whereas the latter lack the accuracy required for dense crowd scenarios.To address these issues,in this paper YOLO ECEL, a lightweight multi-scale feature enhancement algorithm for dense crowd face detection in aerial images, was proposed based on YOLO11n. First, a lightweight backbone EfficientNetV2 E embedded with Efficient Multi-Scale Attention (EMA) was constructed to enhance multi scale face feature extraction. Then, the CCFM Dy neck network was built by integrating Cross-Scale Feature Fusion Module (CCFM) cross scale feature fusion with DySample dynamic upsampling to preserve edge details of small targets. Third an Efficient Multi Receptive Field Feature Enhancement (EfficientMRFFE) module was designed to strengthen feature representation for dense overlapping faces. Finally, the Lightweight Shared Detail-Enhanced Convolutional Detection Head (LSDECD Head) was adopted to effectively reduce computational redundancy. Experimental results on the self built aerial dataset UAV DenseFace show that, compared with the baseline algorithm, YOLO ECEL improves mAP50 and mAP50:95 by 0.4 and 0.3 percentage points respectively, and reduces the number of parameters, computational cost and model size by 42.3%, 17.2% and 36.5%. Thus, it achieves an efficient and lightweight design while maintaining detection accuracy, and can better support UAV security tasks in dense crowd scenarios.

Key words: Public safety, UAV, Dense face detection, Lightweight object detection, YOLO-ECEL