Enhancing the Safety of UAV Object Detection: A Novel OOD Algorithm for Filtering Prediction Uncertainties in Low-Altitude Airspace

With the rapid development of AI technology in the low-altitude field, the predictive uncertainty of ML models, particularly object-detection models, has become a key bottleneck restricting their airworthiness and safe deployment. A typical manifestation of this is that when facing test samples in open-world scenarios, object-detection models exhibit overconfidence in erroneous predictions. To address this issue, this paper proposes an anomaly-scoring algorithm for out-of-distribution (OOD) evaluation based on augmented deep network features, named PCA-HBOS. By integrating the high-dimensional semantic features extracted by deep networks, the algorithm can score sample distributions, thereby enabling the identification of both in-distribution and out-of-distribution samples. Through comparisons with mainstream OOD algorithms, the superiority of the PCA-HBOS in low-altitude scenarios is validated. Experimental results on three multi-sensor