Cooperative multi-UAV search requires jointly optimizing wide-area coverage, rapid target discovery, and endurance under sensing and motion constraints. Resolving this coupling enables scalable coordination with high data efficiency and mission reliability. We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update, yielding a Markov state that couples agent poses with a probabilistic target field. On this belief–MDP we introduce a segment-conditioned latent-intent framework, in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on the fixed intent; both components are trained end-to-end with proximal updates under a centralized critic. On the $50\times 50$ grid, coverage and discovery convergence times are reduced by up to 48% and 40% relative to a flat actor-critic benchmark, and the aggregated convergence metric improves by about 12% compared with a state-of-the-art hierarchical method. Qualitative analyses further reveal stable spatial sectorization, low path overlap, and fuel-aware patrolling, indicating that segment-conditioned latent intents provide an effective and scalable mechanism for coordinated multi-UAV search.

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection. Current frameworks for oriented detection modules are constrained by intrinsic limitations, including excessive computational and memory overheads, discrepancies between predefined anchors and ground truth bounding boxes, intricate training processes, and feature alignment inconsistencies. To overcome these challenges, we present ASL-OOD (Angle-based SIOU Loss for Oriented Object Detection), a novel, efficient, and robust one-stage framework tailored for oriented object detection. The ASL-OOD framework comprises three core components: the Transformer-based Backbone (TB), the Transformer-based Neck (TN), and the Angle-SIOU (Scylla Intersection over Union) based Decoupled Head (ASDH). By leveraging the Swin Transformer, the TB and TN modules offer several key advantages, such as the capacity to model long-range dependencies, preserve high-resolution feature representations, seamlessly integrate multi-scale features, and enhance parameter efficiency. These improvements empower the model to accurately detect objects across varying scales. The ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU, ensuring precise angular consistency and bounding box coherence. This approach effectively harmonizes shape loss and distance loss during the optimization process, thereby significantly boosting detection accuracy. Comprehensive evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP (mean Average Precision) of 80.16 percent, HRSC2016 with an mAP of 91.07 percent, MAR20 with an mAP of 85.45 percent, and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection models. These findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.