面向红外非合作目标的通用增强检测算法OA

To address the critical challenges of the low contrast and limited resolution of small targets and complex back-ground interference in thermal infrared image-based non-cooperative target detection in airport environments,this paper proposes a universal enhancement detection algorithm specifically optimized for infrared non-cooperative targets.The proposed algorithm systematically improves the baseline model using four core plug-and-play modules.First,a small-scale detection head was introduced to significantly enhance the perception capabilities and localization precision of mi-croscopic targets.Second,a space-to-depth convolution(SPDConv)module was employed to replace the traditional downsampling layers,which effectively preserved the fine-grained image features and mitigated the loss of critical de-tails.Third,a reparameterized large-kernel inverted bottleneck(RepLKIB)module was designed.This module utilizes a parallel multi-branch structure to extract multi-scale features by incorporating a large-kernel convolutional branch to ex-pand the receptive field.Furthermore,an inverted bottleneck structure was adopted to enhance feature representation,whereas structural reparameterization technology was applied to fuse the multi-branch training structure into a single-path inference structure,thereby balancing the accuracy and computational efficiency.Finally,an efficient intersection-over-union loss function was implemented to replace the complete intersection-over-union loss function.By decoupling the center and scale error terms,this approach significantly strengthened the regression gradients for low-quality samples,particularly small targets,thus improving the localization robustness and detection rates.Comparative experi-ments conducted on a self-constructed comprehensive infrared non-cooperative target dataset demonstrated the superior performance of the proposed algorithm.Building on the YOLOv10n(nano-scale)baseline,the mAP@[0.5:0.95]met-ric increased substantially by 9.1 percentage points,reaching 94.0%.When compared with the larger YOLOv10m(medium-scale)model,the proposed model reduced the number of parameters and inference time to 70.3%and 75.7%of the original values,respectively,while incurring only a marginal 0.5%decrease in accuracy.A comparison with other versions of the YOLO series indicated that the algorithm achieved an accuracy comparable to that of medium-scale mod-els while maintaining nano-scale inference speeds.Additional experiments using YOLOv11 as the baseline further vali-dated the effectiveness and universality of the proposed modules across different architectures.In conclusion,this algo-rithm provides an efficient and reliable solution for infrared non-cooperative target detection in complex scenarios.