Object detection in complex environments, such as challenging lighting conditions, adverse weather, and target occlusions, poses significant difficulties for existing algorithms. To address these challenges, this study introduces a collaborative solution integrating improved CycleGAN-based data augmentation and an enhanced object detection framework, AS-YOLO. The improved CycleGAN incorporates a dual self-attention mechanism and spectral normalization to enhance feature capture and training stability. The AS-YOLO framework integrates a channel–spatial parallel attention mechanism, an AFPN structure for improved feature fusion, and the Inner_IoU loss function for better generalization. The experimental results show that compared with YOLOv8n, mAP@0.5 and mAP@0.95 of the AS-YOLO algorithm have increased by 1.5% and 0.6%, respectively. After data augmentation and style transfer, mAP@0.5 and mAP@0.95 have increased by 14.6% and 17.8%, respectively, demonstrating the effectiveness of the proposed method in improving the performance of the model in complex scenarios.

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience.