LCM-YOLO: A Small Object Detection Method for UAV Imagery Based on YOLOv5

Abstract

This study addresses the challenges of detecting small targets and targets with significant scale variations in UAV aerial images. We propose an improved YOLOv5 model, named LCM-YOLO, to tackle these challenges. Initially, a local fusion mechanism is introduced into the C3 module, forming the C3-LFM module to enhance feature information acquisition during feature extraction. Subsequently, the CCFM is employed as the neck structure of the network, leveraging its lightweight convolution and cross-scale feature fusion characteristics to effectively improve the model's ability to integrate target features at different levels, thereby enhancing its adaptability to scale variations and detection performance for small targets. Additionally, a multi-head attention mechanism is integrated at the front end of the detection head, allowing the model to focus more on the detailed information of small targets through weight distribution. Experiments on the VisDrone2019 dataset show that LCM-YOLO has excellent detection capabilities. Compared to the original YOLOv5 model, its mAP50 and mAP50-95 metrics are improved by 7.2% and 5.1%, respectively, reaching 40.7% and 22.5%. This validates the effectiveness of the LCM-YOLO model for detecting small and multi-scale targets in complex backgrounds.