Thermal drones help to search for missing people in the wilderness, but flying high enough to cover wide areas shrinks each person to just a few pixels, causing standard detectors to miss them. We introduce MicroSlicer, a lightweight method that improves small-target detection by slicing each frame into overlapping tiles at inference time, with no extra training required. Tested on three detector architectures (YOLO26-nano, YOLO26-small, RT-DETR-Large) using the WiSARD thermal search-and-rescue dataset, MicroSlicer improves target recall (+11.7 to +18.9 percentage points overall and up to +22.7 percentage points on small targets) over standard full-frame inference. High-resolution scaling experiments further indicate that, while MicroSlicer yields higher small-target recall on YOLO26s and RT-DETR-L, single-pass full-frame inference at 1536 px achieves higher recall and throughput on the ultra-lightweight YOLO26n, demonstrating that the trade-off between spatial slicing and global input scaling depends on the model capacity and architecture. When benchmarked against an existing slicing framework, SAHI, with matched non-maximum suppression (NMS) and a slices-only configuration, MicroSlicer achieves similar overall detection accuracy while maintaining higher target recall across all architectures and reducing the post-processing latency via compiled C++ tensor operations. Furthermore, on Transformer detectors under default merging settings, SAHI exhibits substantial latency increases and bounding-box dilation at low confidence thresholds, whereas MicroSlicer maintains stable deduplication ($0.11–0.14$ ms at operational confidence and $2.1–3.6$ ms under dense candidate sets at low thresholds) and consistent accuracy. We also found that full-image training combined with MicroSlicer performed best for YOLO26s and RT-DETR-L (by up to 21.0 percentage points over patch-trained models), whereas YOLO26n benefited from sliced training. While currently evaluated on a single thermal dataset using a desktop GPU workstation, these results establish MicroSlicer as a practical, training-free option for edge-assisted search-and-rescue operations, with field validation on low-power embedded hardware identified as essential future work.