To improve traffic monitoring efficiency, particularly in situations where the field of view of fixed road cameras is limited, a drone-based traffic monitoring system using the PLCnext platform is proposed. This system integrates YOLOv9 deep learning technology, enabling two UAVs equipped with cameras to cooperate with the PLCnext platform. This collaboration provides broader coverage and more flexible monitoring capabilities for real-time vehicle surveillance. To enhance detection accuracy in low-resolution aerial images, a new model, YOLOv9s-SPDADown-LSK, is introduced. The model utilizes a SPD layer to improve image detail retention, optimizes the downsampling process with the ADown module, and incorporates the LSK attention mechanism in the backbone network to enhance feature extraction. Experimental results indicate that the system achieves an image processing delay of approximately 80 ms, with the modified model reaching mAP@0.5 and mAP@0.5:0.95 values of 96.3% and 82.7%, respectively. The detection accuracy stands at 97.2%, significantly reducing false positives and false negatives, thereby validating the system′s feasibility and the effectiveness of the proposed algorithm.