Steel surface defect detection is crucial in manufacturing, but achieving high accuracy and real-time performance with limited computing resources is challenging. To address this issue, this paper proposes DFFNet, a lightweight fusion network, for fast and accurate steel surface defect detection. Firstly, a lightweight backbone network called LDD is introduced, utilizing partial convolution to reduce computational complexity and extract spatial features efficiently. Then, PANet is enhanced using the Efficient Feature-Optimized Converged Network and a Feature Enhancement Aggregation Module (FEAM) to improve feature fusion. FEAM combines the Efficient Layer Aggregation Network and reparameterization techniques to extend the receptive field for defect perception, and reduce information loss for small defects. Finally, a WIOU loss function with a dynamic non-monotonic mechanism is designed to improve defect localization in complex scenes. Evaluation results on the NEU-DET dataset demonstrate that the proposed DFFNet achieves competitive accuracy with lower computational complexity, with a detection speed of 101 FPS, meeting real-time performance requirements in industrial settings. Furthermore, experimental results on the PASCAL VOC and MS COCO datasets demonstrate the strong generalization capability of DFFNet for object detection in diverse scenarios.

钢材表面缺陷检测在制造中至关重要，但利用有限的计算资源实现高精度和实时性能具有挑战性。为了解决这个问题，本文提出了轻量级融合网络 DFFNet，用于快速准确的钢材表面缺陷检测。首先，引入了一种名为LDD的轻量级骨干网络，利用部分卷积来降低计算复杂度并有效地提取空间特征。然后，使用高效特征优化融合网络和特征增强聚合模块 (FEAM) 来增强 PANet，以改善特征融合。 FEAM结合了高效层聚合网络和重参数化技术来扩展缺陷感知的感受野，并减少小缺陷的信息丢失。最后，设计了具有动态非单调机制的WIOU损失函数，以改善复杂场景中的缺陷定位。 NEU-DET数据集上的评估结果表明，所提出的DFFNet以较低的计算复杂度实现了有竞争力的精度，检测速度为101 FPS，满足工业环境中的实时性能要求。此外，在 PASCAL VOC 和 MS COCO 数据集上的实验结果证明了 DFFNet 在不同场景下的目标检测方面具有强大的泛化能力。