Accurate detection of distant drones requires a compact network that preserves weak visual cues. We combine a separable context pyramid (SCP), dual-context energy attention (DCEA), and geometry-calibrated regression (GCR) within a YOLO11n detector. Across repeated training runs on UAV-6E, the full model reached 94.25% validation mAP@0.5 (SD 0.29%), compared with 91.13% (SD 0.59%) for YOLO11n and 93.58% (SD 0.21%) for SCP alone. SCP supplied most of the gain. In the external DUT-2200 evaluation, full-model AP@0.75 was 54.55% (SD 0.47%) versus 51.13% (SD 1.80%) for YOLO11n. The full detector used 2.33 million parameters and 5.27 GFLOPs at 640 by 640 pixel input, compared with 2.59 million and 6.44 GFLOPs for YOLO11n. The embedded implementation achieved 26.3 FPS on the Orange Pi 5 Plus RK3588 platform, supporting practical camera-based UAV monitoring.

Detecting unmanned aerial vehicles (UAVs) in complex low-altitude scenes remains challenging due to limited target details and background interference. To address these challenges, this paper proposes DARA-Net, a lightweight UAV detection framework that coordinates detail preservation, efficient feature extraction, and multiscale aggregation. Built upon YOLOv11, DARA-Net integrates adaptive window reduction (AWR) to retain informative local features during downsampling, a separable aggregation block (SAB) to improve feature representation with fewer parameters, and a directional weighted pyramid (DWP) to enhance cross-scale feature fusion. Experiments on a visible-light UAV dataset containing 25,941 images demonstrate that DARA-Net achieves 97.0% precision, 91.1% recall, 95.1% mAP@0.5, and 62.0% mAP@0.5:0.95. Compared with the YOLOv11 baseline, recall improves by 2.6 percentage points, while the parameter count decreases by approximately 30.6% and model size decreases by 29.1%, reaching 1.79 M parameters and 3.9 MB, respectively. Compared with YOLOv26n, DARA-Net achieves a higher mAP@0.5:0.95 (62.0% versus 60.77%) with approximately 24.6% fewer parameters and a 28.2% smaller model file, while maintaining comparable mAP@0.5. Additional validation on an external UAV dataset further supports its competitive detection performance. These results highlight the value of DARA-Net as a compact UAV detector that prioritizes localization performance and reduced parameter and storage requirements.

Parallel sensing can shorten multi-UAV mapping missions, yet late-stage exploration remains costly when low-yield frontiers continue to trigger flight, reassignment, and communication. We formulate this problem as pre-allocation candidate regulation under sparse summary sharing and propose \method. Each frontier is represented by an inspectable record containing gain, viewpoint quality, travel cost, and peer-state information. Normalized gain evidence lowers the assignment cost within a fixed cap, and an auxiliary peer-connectivity term penalizes moves that increase the peer-distance burden. The allocator selects the eligible candidate with minimum adjusted cost; if none is available, the UAV retains its current target or receives no new assignment. We evaluate the method in three forest-like 3D simulation scenes using five matched runs per scene. Compared with the dense-communication reference (\dcr), final volume was $0.02\%$ lower, while completion time, flight distance, communication volume, and message count decreased by $12.72\%$, $12.01\%$, $37.54\%$, and $29.61\%$, respectively. Compared with the sparse-sharing baseline (\ssb), final volume was $0.08\%$ lower; completion time, flight distance, and message count decreased by $13.26\%$, $12.85\%$, and $7.32\%$, respectively. Communication volume decreased by $2.53\%$, but its confidence interval crossed zero. Module ablations indicate that gain evidence and reward capping both contribute to the observed regulation behavior. Removing the peer-connectivity term produced no significant paired change, so we treat it as an auxiliary regularizer.