LMEC-YOLOv8: An Enhanced Object Detection Algorithm for UAV Imagery

作者：Xuchuan Tai, Xinjun Zhang · 发表于：Electronics · 年份：2025 · DOI：10.3390/electronics14132535 · 被引用次数：5 · 研究领域：Advanced Neural Network Applications、Infrared Target Detection Methodologies、Robotics and Sensor-Based Localization

Despite the rapid development of UAV (Unmanned Aerial Vehicle) technology, its application for object detection in complex scenarios faces challenges regarding the small target sizes and environmental interference. This paper proposes an improved algorithm, LMEC-YOLOv8, based on YOLOv8n, which aims to enhance the detection accuracy and real-time performance of UAV imagery for small targets. We propose three key enhancements: (1) a lightweight multi-scale module (LMS-PC2F) to replace C2f; (2) a multi-scale attention mechanism (MSCBAM) for optimized feature extraction; and (3) an adaptive pyramid module (ESPPM) and a bidirectional feature network (CBiFPN) to boost fusion capability. Experimental results on the VisDrone2019 dataset demonstrate that LMEC-YOLOv8 achieves a 10.1% improvement in mAP50, a 20% reduction in parameter count, and a frame rate of 42 FPS compared to the baseline YOLOv8n. When compared to other state-of-the-art algorithms, the proposed model achieves an optimal balance between accuracy and speed, validating its robustness and practicality in complex environments.