Table 1.
Structural comparison between representative enhancement strategies and the proposed modules. Overhead figures for SPPELAN, MFCA, and MLLA are measured relative to the YOLOv11n baseline (Table 2); overhead descriptions for other methods reflect quantities reported in their original publications.
Table 2.
Computational overhead of each proposed module relative to the YOLOv11n baseline. Latency is measured as single-image inference time (ms) on an NVIDIA RTX 3090 GPU with batch size 1 and input resolution .
Fig 1.
Overall architecture of MLE-YOLOv11n.
Three targeted module substitutions are applied at distinct architectural positions: SPPELAN replaces SPPF at the P5 backbone terminus; MFCA replaces equal-weight Concat at each PANet neck fusion node; the MLLA block replaces the standard convolutional prediction block at the P4-scale detection head. P3 and P5 heads retain the standard decoupled structure. Each substitution targets a specific structural bottleneck independently, and their combination yields mAP gains exceeding those of any single module in isolation, as confirmed by the ablation study.
Fig 2.
Structure of the SPPELAN module.
Input P5 is compressed by CV1, then passed through three successive MaxPool layers () in a layer-aggregation cascade. All four intermediate outputs
are independently retained, concatenated, and compressed by CV5 to produce the enhanced backbone terminus feature
.
Fig 3.
Overall data flow of the MFCA module.
Each input feature stream passes independently through FEM for multi-branch enhancement. CRC performs learnable channel-wise weighted fusion of the enhanced streams to produce fused representation H. SCAM subsequently introduces cross-region spatial context via Global Max Pooling (GMP), Global Average Pooling (GAP), and Key–Value attention, producing context increment d that is residually added to H to yield the final output Fout.
Fig 4.
Structure of the Feature Enhancement Module (FEM).
Four parallel branches with complementary kernel configurations extract local spatial features (Branch 0), horizontal structural features (Branch 1), vertical structural features (Branch 2), and a linear identity projection (Branch 3). Branch outputs are concatenated, compressed by ConvLinear, and fused with the input via a scaled residual connection ().
Fig 5.
Structure of the Channel Rewrite Connection (CRC).
A learnable weight vector of length (or
for three-input nodes) is L1-normalized to per-channel fusion coefficients
, which are applied channel-wise to the concatenated FEM-enhanced feature streams to produce the adaptively weighted fused representation H.
Fig 6.
Structure of the Spatial Context-Aware Module (SCAM).
GMP and GAP produce complementary global channel statistics; combined with Key–Value attention, they generate context increment d that is residually added to the CRC-fused feature H to produce the final MFCA output Fout.
Fig 7.
Structure of the MLLA detection head.
The P4 feature map is partitioned into local windows and processed by the Irregular Serpentine Scan to reorder windows into a sequence where cross-row boundaries are spatially connected. Scaled dot-product attention is applied within the reordered window sequence. The attention output is projected back to the spatial layout and fused with the original input via a residual connection with Layer Normalization. P3 and P5 scale heads use the standard decoupled convolutional structure.
Fig 8.
Representative samples from the CPLID and IDID datasets.
(a) Normal insulators (CPLID) under diverse backgrounds. (b) Defective insulators (CPLID). (c1) Good insulator shell (IDID). (c2) Broken insulator shell (IDID). (c3) Flashover-damaged insulator shell (IDID). On the right is the data augmentation display.
Table 3.
Class distribution of the CPLID dataset across subsets after stratified splitting and augmentation. All subsets including the test set are augmented to expand the evaluation pool.
Table 4.
Module ablation on the CPLID dataset. Checkmarks (✓) indicate module inclusion; dashes (–) denote the baseline without the respective module. Precision (P) and Recall (R) are in percentage points.
Table 5.
Hyperparameter sensitivity analysis on the CPLID validation set. One hyperparameter is varied at a time; all other settings are fixed at their default values (, d = 5, w = 8). Selected values are in bold.
Table 6.
Comparison with state-of-the-art detectors on the CPLID test set. All methods were retrained under identical dataset splits and input resolution (). Best results are in bold. MLE-YOLOv11n results are reported as mean over three independent training runs; standard deviations are given in the text.
Fig 9.
Accuracy-efficiency trade-off on the CPLID test set.
The scatter plot shows mAP@50 versus parameter count (left) and FPS versus mAP@50 (right) for all evaluated methods. MLE-YOLOv11n (star marker) occupies the Pareto-optimal position in both panels, achieving the highest mAP@50 among all models while maintaining parameter count comparable to the lightest YOLO variants and inference speed suitable for real-time UAV deployment.
Table 7.
Comparison with state-of-the-art detectors on the IDID test set. All methods were evaluated under the same configuration as in Table 6. Best results are in bold. MLE-YOLOv11n results are reported as mean over three independent training runs; standard deviations are given in the text.
Table 8.
Per-category AP@50 on the CPLID test set. The largest gains are concentrated in the rare defective class, consistent with the MFCA module’s targeted enhancement of small-scale and compositionally complex targets.
Table 9.
Per-category AP@50 on the IDID test set. Improvement is largest for the rare and structurally complex Broken insulator shell category, consistent with MFCA’s adaptive feature emphasis under class imbalance.
Fig 10.
Qualitative analysis: (a) detection results comparing YOLOv11n baseline (red boxes) and full MLE-YOLOv11n (green boxes) across three defect categories; improvements are most pronounced for the rare Broken and Defective categories.
(b) Attention heatmaps showing progressive refinement through cumulatively added modules (+SPPELAN, + SPPELAN+MFCA, + SPPELAN+MFCA+MLLA) on two IDID samples; the monotonic attention concentration toward defect regions confirms the complementary and independent contribution of each module.