优化改进YOLOv8实现实时无人机车辆检测的算法OA北大核心CSTPCD

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.