Correction: Wang et al. Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD. Drones 2025, 9, 341

Missing Citation

In the original publication [1], the reference “Zhang, Z.; Xu, Y.; Song, J.; Zhou, Q.; Rasol, J.; Ma, L. Planet craters detection based on unsupervised domain adaptation. IEEE Trans. Aerosp. Electron. Syst. 2023, 59, 7140–7152” was not cited. The citation has now been inserted in “2. Related Work”, the fourth paragraph, and should read as follows:

“Despite some significant progress in air-to-ground visual target tracking, many challenges remain. For example, the SORT algorithm, which uses convolutional neural networks for object detection and Kalman filters to predict object motion, frequently encounters target ID changes during long-term tracking, particularly performing poorly in occlusion scenarios [20]. DeepSORT improves on this issue by introducing re-identification features and cascade matching strategies, but it still cannot completely avoid ID switching problems. Other methods such as MOTDT, ByteTrack, and BoTSORT show improvements in specific scenarios, but they still fall short in handling weak small-target detection, non-linear trajectory prediction, and trajectory continuity. Additionally, many joint detection MOT methods like JDE, FairMOT, and CenterTrack improve inference speed, but they still face challenges in accuracy and robustness when dealing with target scale variations, occlusions, and complex background interference. In summary, current methods have significant room for improvement in weak small-target detection, non-linear trajectory prediction, and trajectory continuity. The proposed method in this paper effectively addresses these issues by combining convolutional attention modules, global context enhancement, and adaptive, bidirectional, long short-term memory networks, significantly improving the precision and robustness of multi-target tracking; the algorithm structure is shown in Figure 2. Compared to the aforementioned classical methods, the proposed approach achieves an improvement of over 3% in the MOTA metric, particularly demonstrating a 20% reduction in ID switches under scenarios involving occlusion and small/weak targets.”

The “References” section will be updated with the addition of the above-mentioned reference as follows:

20.

Zhang, Z.; Xu, Y.; Song, J.; Zhou, Q.; Rasol, J.; Ma, L. Planet craters detection based on unsupervised domain adaptation. IEEE Trans. Aerosp. Electron. Syst. 2023, 59, 7140–7152.

With this correction, the order of some references has been adjusted accordingly. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.