基于改进YOLOv5s的无人机小目标检测算法研究

Research on UAV Small Target Detection Algorithm Based on Improved YOLOv5s

【目的】针对无人机航拍图像中目标尺度多样、背景复杂、小目标密集的特点,提出了基于YOLOv5s的小目标检测算法LM-YOLO。【方法】首先,增加小目标检测头并采用K-DBSCAN聚类算法优化锚框,生成更适合小目标检测的锚框,提高算法对小目标的检测精度;然后,设计更高效的MobileNetV3-CBAM作为特征提取网络,减小网络模型大小;最后,在特征融合网络引入大核选择性注意力机制LSK,增加模型对相似目标的分辨率。【结果】在公开数据集VisDrone2019上的实验结果表明,与基准模型YOLOv5s相比,LM-YOLO对所有目标的平均检测精度提升了7.6%,模型大小压缩了45%。【结论】文章算法可以在降低模型大小的同时保持良好的检测精度。

【Objective】Aiming at the characteristics of various target scales,complex background and dense small targets in aerial images of unmanned aerial vehicles(UAC),a small target detection algorithm LM-YOLO based on YOLOV5 is proposed.【Method】Firstly,the number of small target detection head was increased and K-DBSCAN clustering algorithm was used to optimize the anchor frame,so as to generate an anchor frame more suitable for small target detection and improve the detection accuracy of the algorithm.Then,a more efficient MobileNetV3-CBAM was designed as a feature extraction network to reduce the size of the network model.Fi-nally,the large kernel selective attention mechanism LSK was introduced into the feature fusion network to in-crease the resolution of the model to similar targets.【Result】The experimental results on the public data set Vis-Drone2019 show that the average detection accuracy of LM-YOLO for all targets is improved by 7.6%and the model size is reduced by 45%compared with the benchmark model YOLOV5.【Conclusion】Experiments show that the proposed algorithm can reduce the model size while maintaining good detection accuracy.