Objectives The critical factor in the safe landing of carrier-based aircraft is the successful locking of the tailhook and arresting wires. However, in the existing research, there is relatively little work on using intelligent means to assist the landing signal officer (LSO) in identifying the arrested landing state.

Method This paper proposes a model for identifying the arrested landing state which integrates coordinate attention (CA) and a weighted bi-directional feature pyramid network (BiFPN). First, CA is used to enhance the network's feature extraction ability in both the spatial and channel dimensions. Next, BiFPN introduces learnable weights to learn the weights of different input features by repeatedly using top-down and bottom-up multi-scale feature fusion. A C2F lightweight model structure is adopted to reduce the parameters and computational complexity. Finally, the proposed model is compared with five baseline models through simulation experiments.

Results The results reveal that the proposed model outperforms the baseline model in detecting the tailhook and arresting wires of carrier-based aircraft.

Conclusions The findings of this study can provide valuable references for improving the accuracy and robustness of the detection of the tailhook and arresting wires of carrier-based aircraft, and is of great significance for improving the efficiency of carrier-based aircraft landing operations and preventing potential personnel injuries and equipment losses.