摘要： 针对工业现场钢材表面缺陷对比度低、尺度变化大以及端侧算力受限等挑战，本文提出一种全路径协同增强的高效检测网络ISA-DETR。首先，构建信息保真下采样（Information-Preserving Downsampling,IPD）结构，采用空间-通道重排的像素重组方式替代传统步长下采样，在降低特征图分辨率的同时有效保留细粒度空间信息，缓解微小缺陷在特征提取过程中的信息丢失问题。其次，设计集成大核可分离注意力机制的SLK-HG（Large Separable Kernel Attention-Hybrid Group Block）模块，通过分组卷积与可分离卷积的协同优化，以近似线性计算复杂度构建超大感受野，增强网络对长程空间依赖及不规则缺陷形态的建模能力。最后，引入自适应动态采样（Adaptive Dynamic Sampling, ADS）算子，通过内容驱动的偏移预测实现跨尺度特征的精确对齐，减少复杂背景下的定位偏差，提升检测鲁棒性。在NEU-DET钢材表面缺陷数据集上的实验结果表明，在参数量仅为20.67M、计算量为77.5GFLOPs的条件下，ISA-DETR的检测精度达到75.2%的mAP@0.5。相较于基准模型，其参数量和计算量分别降低35.4%和25.1%，同时检测精度提升3.2%。此外，在PCB缺陷数据集上的迁移实验进一步验证了该方法良好的泛化能力。所提出算法在检测性能与部署效率之间实现了有效平衡，为工业端侧智能质检提供了一种高效可靠的解决方案。

Abstract: To address the challenges of low contrast, large-scale variation, and limited computational resources in industrial steel surface defect inspection, this paper proposes an efficient detection network with full-path collaborative enhancement, termed ISA-DETR. First, an Information-Preserving Downsampling (IPD) structure is constructed, which adopts a spatial–channel rearrangement-based pixel reorganization strategy to replace conventional strided downsampling. This design effectively preserves fine-grained spatial information while reducing feature map resolution, thereby alleviating the information loss of small defects during feature extraction. Second, a Large Separable Kernel Attention-Hybrid Group Block (SLK-HG) module is developed by integrating large-kernel separable attention mechanisms. Through the collaborative optimization of group convolution and separable convolution, the module builds an ultra-large receptive field with near-linear computational complexity, significantly enhancing the network’s ability to model long-range spatial dependencies and irregular defect patterns. Furthermore, an Adaptive Dynamic Sampling (ADS) operator is introduced to achieve precise cross-scale feature alignment via content-driven offset prediction, reducing localization deviations in complex backgrounds and improving detection robustness. Experimental results on the NEU-DET steel surface defect dataset demonstrate that ISA-DETR achieves an mAP@0.5 of 75.2% with only 20.67M parameters and 77.5 GFLOPs. Compared with the baseline model, the proposed method reduces the number of parameters and computational cost by 35.4% and 25.1%, respectively, while improving detection accuracy by 3.2%. In addition, transfer experiments on the PCB defect dataset further verify the strong generalization capability of the proposed approach. Overall, the proposed method achieves an effective balance between detection performance and deployment efficiency, providing a practical and reliable solution for intelligent quality inspection in industrial edge scenarios.