Experimental environment configuration.

Steel surface defect detection is an important application of object detection in industry. Achieving object detection in industry while balancing detection accuracy and real-time performance is a challenge. Therefore, this paper proposes an improved FasterNet-YOLO model based on the one-stage detector. Introduce the FasterNet network to reconstruct the YOLOv5 backbone network. Achievement of model lightweighting and significant improvement in detection speed, but with a slight reduction in accuracy. The YOLOv5 neck network’s ordinary convolution is improved by depthwise separable convolution. Continuing to improve detection speed while further reducing redundant parameters in the neck network. To improve model accuracy, the Swin-Transformer is integrated into the C3 module in the neck network. Solve the problem of cluttered backgrounds in defect photographs and easy confusion between defect types. Meanwhile, BiFPN is used for feature fusion. By retaining more informative features, the detector’s ability to adapt to targets at different scales is improved. The results indicated that when comparing FasterNet-YOLO with the original model, the parameters were reduced by 49.4%, GFLOPs were reduced by 57.0%, mAP increased by 6.2%, and FPS increased by 54.1%. The improved model not only increases the detection accuracy, but also significantly improves the speed of hot-rolled strip surface defect detection to meet the requirements of real-time detection.

钢材表面缺陷检测是目标检测技术在工业领域的重要应用场景。在工业场景中实现目标检测并同时兼顾检测精度与实时性，向来是一项极具挑战性的任务。为此，本文提出了一种基于单阶段检测器的改进型FasterNet-YOLO模型。引入FasterNet网络重构YOLOv5的主干网络，可实现模型轻量化并显著提升检测速度，但会带来精度的小幅下降。针对YOLOv5的颈部网络，将普通卷积替换为深度可分离卷积，在进一步降低颈部网络冗余参数的同时，持续提升检测速度。为提升模型精度，将Swin Transformer集成至颈部网络的C3模块中，以此解决缺陷图像背景杂乱、缺陷类型间易混淆的问题。同时，采用双向特征金字塔网络（BiFPN）进行特征融合，通过保留更多有效特征，提升检测器对不同尺度目标的适配能力。实验结果表明，相较于原始模型，改进后的FasterNet-YOLO模型参数规模降低49.4%，十亿次浮点运算量（GFLOPs）减少57.0%，平均精度均值（mAP）提升6.2%，帧率（FPS）提升54.1%。该改进模型不仅提升了检测精度，还显著加快了热轧带钢表面缺陷检测的速度，能够满足实时检测的需求。