计算机工程与应用2024，Vol.60Issue(9)：79-89,11.DOI:10.3778/j.issn.1002-8331.2312-0291
优化改进YOLOv8实现实时无人机车辆检测的算法
Algorithm for Real-Time Vehicle Detection from UAVs Based on Optimizing and Improving YOLOv8
摘要
Abstract
To address the problems of low accuracy,easy interference from background environment and difficulty in detecting small target vehicles of existing UAV vehicle detection algorithms,an improved UAV vehicle detection algo-rithm YOLOv8-CX is proposed based on YOLOv8.By integrating the advantages of Deformable Convolutional Networks v1-3,a C2f-DCN module is proposed to flexibly sample features and better extract features between vehicles of different sizes.Utilizing the idea of large separable kernel attention,a SPPF-LSKA module is proposed with long-range dependency and self-adaptability,which can effectively reduce background interference on vehicle detection.In the neck network,a CF-FPN(ment network for tiny object deteciton)feature fusion structure is adopted to enhance the detection accuracy of small targets by combining contextual information and suppressing conflicts between features at different scales.Finally,the original YOLOv8 head is replaced with a Dynamic Head detection head.By unifying scale,space and task,the three types of attention mechanisms,the model detection performance is further improved.Experimental results show that on the Mapsai dataset,compared with the original algorithm,the improved algorithm increases the accuracy(P),recall(R)and mean average precision(mAP)by 8.5,11.2 and 6.2 percentage points respectively,and the algorithm detection speed reaches 72.6 FPS,meeting the real-time requirements of UAV vehicle detection.By comparing with other mainstream tar-get detection algorithms,the effectiveness and superiority of this method are validated.关键词
无人机车辆检测/YOLOv8/可变形卷积/注意力机制/特征融合Key words
unmanned vehicle detection/YOLOv8/deformable convolution/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
史涛,崔杰,李松..优化改进YOLOv8实现实时无人机车辆检测的算法[J].计算机工程与应用,2024,60(9):79-89,11.基金项目
国家自然科学基金(61203343). （61203343）
