<p>Reliable defect detection of miniature components in power distribution networks remains challenging due to extreme scale variation, progressive loss of fine-grained spatial details in deep feature hierarchies, and structured background interference from complex field environments. This paper proposes ACSFANet, an Adaptive Cross-Scale Feature Aggregation Network that addresses these issues through three synergistic innovations. First, an adaptive cross-scale feature aggregation module employs deformable scale alignment, channel-spatial joint adaptive weighting, and cross-level gated fusion to enable content-conditioned routing of information across pyramid levels, dynamically suppressing or amplifying cross-scale pathways based on target scale characteristics. Second, a detail-preserving detection branch operating at stride-2 resolution retains high-resolution spatial representations critical for sub-16-pixel defect localization. Third, a scale-balanced loss function coupled with dynamic label assignment ensures that gradient signals from tiny, underrepresented defect instances are neither overwhelmed by larger targets nor artificially inflated. Experiments on the DNMD dataset—12,836 UAV-captured images across six defect categories—show that ACSFANet reaches 50.3% mAP and 35.8% APsmall (the COCO definition, i.e. ground-truth boxes with area below 32 × 32 pixels). On mAP the model exceeds the strongest baseline in our comparison, YOLO11-L, by 2.3% points, and on APsmall it exceeds the strongest APsmall baseline, again YOLO11-L, by 5.3 points; against the transformer-based Co-DETR the same two gaps widen to 4.2 and 7.3 points, so the choice of reference baseline visibly changes how large the improvement looks. Ablation studies confirm complementary contributions from each proposed component, and gate activation analysis reveals that the network autonomously learns target-size-dependent cross-scale routing without explicit supervision. </p>