In view of the problems that the existing tower view target detection system is prone to large positioning deviation and low small target detection accuracy, this paper proposes an aircraft target detection method based on the ECIOU structure embedded in the YOLO v8 model from the tower view to improve the accuracy and efficiency of detection. Based on the traditional YOLO v8 model, the CBAM module is first added to enhance the discriminability of target features. Then, the GConv and SENet attention mechanisms are introduced to optimize the Bottleneck structure to enhance its feature extraction ability. Thirdly, the ECIOU Loss is used to replace the original CIOU loss function to improve its detection performance in complex environments. Lastly, the small target detection head PWHead is reconstructed to better capture the details of small targets. The model is evaluated on the Roboflow public dataset and its performance is compared with other mainstream models. The experimental results show that the accuracy of the improved YOLO v8 is 90.2%, and the mAP@50 is 86.9%, which is 2.2% and 1.3% higher than that of YOLO v8n respectively, and the detection efficiency is improved. This provides reliable technical support for remote towers to monitor aircraft in real-time.